Introduction to Remote Sensing- Remote sensing” is the science (and to some extent, art) of acquiring information about the Earth's surface without actually being in contact with it.

1.1 What is Remote Sensing?
1.2 Electromagnetic Radiation
1.3 Electromagnetic Spectrum
1.4 Interactions with the Atmosphere
1.5 Radiation - Target
1.6 Passive vs. Active Sensing
1.7 Characteristics of Images
1.8 Endnotes
1. Introduction to fundamentals
2. Sensors
2.1 On the Ground, In the Air, In Space
2.2 Satellite Characteristics
2.3 Pixel Size, and Scale
2.4 Spectral Resolution
2.5 Radiometric Resolution
2.6 Temporal Resolution
2.7 Cameras and Aerial Photography
2.8 Multispectral Scanning
2.9 Thermal Imaging
2.10 Geometric Distortion
2.11 Weather Satellites
2.12 Land Observation Satellites
2.13 Other Sensors
2.14 Data Reception

3.1 Introduction
3.2 Visual interpretation
3.3 Digital processing
3.4 Preprocessing
3.5 Enhancement
3.6 Transformations
3.7 Classification
3.8 Integration
3. Image Analysis 4. Applications
4.1 Introduction
4.2 Hydrology
- Flood Delineation
- Soil Moisture
4.3 Land Cover
- Rural & Urban Planning
- Biomas Estimation

1.1 What is Remote Sensing?
"Remote sensing” is the science (and to some extent, art) of acquiring information about the Earth's
surface without actually being in contact with it. This is done by sensing and recording reflected or
emitted energy and processing, analyzing, and applying that information."
In much of remote sensing, the process involves an interaction between incident radiation
and the targets of interest. This is exemplified by the use of imaging systems where the
following seven elements are involved. Note, however that remote sensing also involves the
sensing of emitted energy and the use of non-imaging sensors.
1. Energy Source or Illumination (A)
The first requirement for remote sensing is to have an energy
source which illuminates or provides electromagnetic energy
to the target of interest.

Transmission, Reception, and Processing (E)
The energy recorded by the sensor has to be transmitted,
often in electronic form, to a receiving and processing station
where the data are processed into an image (hardcopy and/or
digital).
Interpretation and Analysis (F)
The processed image is interpreted, visually and/or
digitally or electronically, to extract information about the
target which was illuminated.
Application (G)
The final element of the remote sensing process is achieved when we apply the information we
have been able to extract from the imagery about the target in order to better understand it,
reveal some new information, or assist in solving a particular problem.

2. Radiation and the Atmosphere (B)
As the energy travels from its source to the target, it
will come in contact with and interact with the
atmosphere it passes through. This interaction may
take place a second time as the energy travels from
the target to the sensor.

3. Interaction with the Target (C)
Once the energy makes its way to the target through the
atmosphere, it interacts with the target depending on the
properties of both the target and the radiation.
4. Recording of Energy by the Sensor (D)
After the energy has been scattered by, or emitted from the
target, we require a sensor (remote - not in contact with the
target) to collect and record the electromagnetic radiation.

1.2 Electromagnetic Radiation
As was noted in the previous section, the first requirement
for remote sensing is to have an energy source to
illuminate the target (unless the sensed energy is being
emitted by the target). This energy is in the form of
electromagnetic radiation.
All electromagnetic radiation has fundamental properties
and behaves in predictable ways according to the basics of
wave theory.
Electromagnetic radiation consists of an electrical field(E)
which varies in magnitude in a direction perpendicular to
the direction in which the radiation is traveling, and a
magnetic field (M) oriented at right angles to the electrical
field. Both these fields travel at the speed of light (c).

Two characteristics of electromagnetic radiation are particularly important for understanding
remote sensing. These are the wavelength and frequency.
The wavelength is the length of one wave cycle, which can be
measured as the distance between successive wave crests.
Wavelength is usually represented by the Greek letter lambda (λ).
Wavelength is measured in metres (m) or some factor of metres
such as nanometres (nm, 10-9 metres), micrometres (µm, 10-6
metres) (µm, 10-6 metres) or centimetres (cm, 10-2 metres).
Frequency refers to the number of cycles of a wave passing a
fixed point per unit of time. Frequency is normally measured in
hertz (Hz), equivalent to one cycle per second, and various
multiples of hertz.
Wavelength and frequency are related by the following formula: 𝒄 = 𝝀𝝊
Where: 𝜆 = wavelength (m);
𝜐 = frequency (cycles per second, Hz)
c = speed of light (3x108 m/s)

Therefore, the two are inversely related to each other. The shorter the wavelength, the higher
the frequency. The longer the wavelength, the lower the frequency. Understanding the
characteristics of electromagnetic radiation in terms of their wavelength and frequency is
crucial to understanding the information to be extracted from remote sensing data. Next we
will be examining the way in which we categorize electromagnetic radiation for just that
purpose.
1.3 The Electromagnetic Spectrum
The electromagnetic spectrum ranges from the shorter wavelengths (including gamma and x-
rays) to the longer wavelengths (including microwaves and broadcast radio waves). There are
several regions of the electromagnetic spectrum which are useful for remote sensing
For most purposes, the ultraviolet or UV portion of the spectrum has the shortest wavelengths
which are practical for remote sensing. This radiation is just beyond the violet portion of the
visible wavelengths, hence its name. Some Earth surface materials, primarily rocks and
minerals, fluoresce or emit visible light when illuminated by UV radiation.

The visible wavelengths cover a range from approximately 0.4 to 0.7 µm.
The longest visible wavelength is red and the shortest is
violet. Common wavelengths of what we perceive as particular colors from
the visible portion of the spectrum are listed below. It is important to note
that this is the only portion of the spectrum we can associate with the
concept of colours.
The light which our eyes - our "remote sensors" - can detect is part of the visible spectrum. It is important to
recognize how small the visible portion is relative to the rest of the spectrum. There is a lot of radiation
around us which is "invisible" to our eyes, but can be detected by other remote sensing instruments and used to
our advantage.
- Violet : 0.4 - 0.446 µm
- Blue : 0.446 - 0.500 µm
- Green : 0.500 - 0.578 µm
- Yellow : 0.578 - 0.592 µm
- Orange: 0.592 - 0.620 µm
- Red :0.620 - 0.7 µm
Blue, green, and red are the primary colours or wavelengths of the visible
spectrum. They are defined as such because no single primary colour can
be created from the other two, but all other colours can be formed by
combining blue, green, and red in various proportions.

Although we see sunlight as a uniform or homogeneous colour, it is
actually composed of various wavelengths of radiation in primarily the
ultraviolet, visible and infrared portions of the spectrum. The visible
portion of this radiation can be shown in its component colours when
sunlight is passed through a prism, which bends the light in differing
amounts according to wavelength.
The next portion of the spectrum of interest is the infrared (IR) region
which covers the wavelength range from approximately 0.7 µm to 100
µm - more than 100 times as wide as the visible portion!
The infrared region can be divided into two categories based on their
radiation properties - the reflected IR, and the emitted or thermal IR.
Radiation in the reflected IR region is used for remote sensing purposes
in ways very similar to radiation in the visible portion.
The reflected IR covers wavelengths from approximately 0.7 µm to 3.0
µm. The thermal IR region is quite different than the visible and reflected
IR portions, as this energy is essentially the radiation that is emitted from
the Earth's surface in the form of heat. The thermal IR covers
wavelengths from approximately 3.0 µm to 100 µm.

The portion of the spectrum of more recent interest to remote
sensing is the microwave region from about 1 mm to 1 m. This
covers the longest wavelengths used for remote sensing. The
shorter wavelengths have properties similar to the thermal
infrared region while the longer wavelengths approach the
wavelengths used for radio broadcasts.
Before radiation used for remote sensing reaches the Earth's
surface it has to travel through some distance of the Earth's
atmosphere. Particles and gases in the atmosphere can affect
the incoming light and radiation. These effects are caused by
the mechanisms of scattering and absorption.
1.4 Interactions with the Atmosphere

a) Scattering
There are three (3) types of scattering which take place.
Scattering occurs when particles or large gas molecules present in the atmosphere
interact with and cause the electromagnetic radiation to be redirected from its
original path. How much scattering takes place depends on several factors including
the wavelength of the radiation, the abundance of particles or gases, and the
distance the radiation travels through the atmosphere.

It occurs when particles are very small compared to the wavelength of the
radiation. These could be particles such as small specks of dust or nitrogen and oxygen
molecules. Rayleigh scattering causes shorter wavelengths of energy to be scattered much
more than longer wavelengths. Rayleigh scattering is the dominant scattering mechanism in
the upper atmosphere.
The fact that the sky appears "blue" during the day is because of this phenomenon. As sunlight
passes through the atmosphere, the shorter wavelengths (i.e. blue) of the visible spectrum are
scattered more than the other (longer) visible wavelengths.
At sunrise and sunset the light has to travel farther through the atmosphere than at midday
and the scattering of the shorter wavelengths is more complete; this leaves a greater
proportion of the longer wavelengths to penetrate the atmosphere
1. Rayleigh scattering

Mie scattering occurs when the particles are just about the same size as the wavelength of the
radiation. Dust, pollen, smoke and water vapour are common causes of Mie scattering which
tends to affect longer wavelengths than those affected by Rayleigh scattering. Mie scattering
occurs mostly in the lower portions of the atmosphere where larger particles are more
abundant, and dominates when cloud conditions are overcast
2. Mie scattering
This occurs when the particles are much larger than the
wavelength of the radiation. Water droplets and large dust
particles can cause this type of scattering. Nonselective
scattering gets its name from the fact that all wavelengths are
scattered about equally.
This type of scattering causes fog and clouds to appear white to
our eyes because blue, green, and red light are all scattered in
approximately equal quantities (blue+green+red light = white
light).
3. Nonselective scattering

2. Absorption
Absorption is the other main mechanism at work when electromagnetic radiation interacts
with the atmosphere. In contrast to scattering, this phenomenon causes molecules in the
atmosphere to absorb energy at various wavelengths. Ozone, carbon dioxide, and water vapour
are the three main atmospheric constituents which absorb radiation.
Ozone serves to absorb the harmful (to most living things)
ultraviolet radiation from the sun. Without this protective layer
in the atmosphere our skin would burn when exposed to
sunlight.
You may have heard carbon dioxide referred to as
a greenhouse gas. This is because it tends to absorb radiation
strongly in the far infrared portion of the spectrum - that area
associated with thermal heating - which serves to trap this
heat inside the atmosphere. Water vapour in the atmosphere
absorbs much of the incoming longwave infrared and
shortwave microwave radiation (between 22µm and 1m).

The presence of water vapour in the lower atmosphere varies greatly from location to location
and at different times of the year. For example, the air mass above a desert would have very little
water vapour to absorb energy, while the tropics would have high concentrations of water
vapour (i.e. high humidity).
Because these gases absorb electromagnetic energy in
very specific regions of the spectrum, they influence
where (in the spectrum) we can "look" for remote
sensing purposes. Those areas of the spectrum which
are not severely influenced by atmospheric absorption
and thus, are useful to remote sensors, are called
atmospheric windows.
By comparing the characteristics of the two most
common energy/radiation sources (the sun and the
earth) with the atmospheric windows available to us,
we can define those wavelengths that we can use most
effectively for remote sensing.

The visible portion of the spectrum, to which our eyes are most sensitive, corresponds to both
an atmospheric window and the peak energy level of the sun. Note also that heat energy
emitted by the Earth corresponds to a window around 10 µm in the thermal IR portion of the
spectrum, while the large window at wavelengths beyond 1 mm is associated with the
microwave region.
Now that we understand how electromagnetic energy makes its journey from its source to the
surface (and it is a difficult journey, as you can see) we will next examine what happens to
that radiation when it does arrive at the Earth's surface.

1.5 Radiation - Target Interactions
Radiation that is not absorbed or scattered in the atmosphere
can reach and interact with the Earth's surface. There are
three (3) forms of interaction that can take place when
energy strikes, or is incident (I) upon the surface.
These are: absorption (A); transmission (T); and reflection
(R). The total incident energy will interact with the surface in
one or more of these three ways. The proportions of each will
depend on the wavelength of the energy and the material and
condition of the feature.

Absorption (A) occurs when radiation (energy) is absorbed into the target while transmission
(T) occurs when radiation passes through a target. Reflection (R) occurs when radiation
"bounces" off the target and is redirected. In remote sensing, we are most interested in
measuring the radiation reflected from targets. We refer to two types of reflection, which
represent the two extreme ends of the way in which energy is reflected from a target: specular
reflection and diffuse reflection.
Absorption (A)

When a surface is smooth we get specular or mirror-like reflection where all (or almost all) of
the energy is directed away from the surface in a single direction. Diffuse reflection occurs
when the surface is rough and the energy is reflected almost uniformly in all directions.
Most earth surface features lie somewhere between perfectly specular or perfectly diffuse
reflectors. Whether a particular target reflects specularly or diffusely, or somewhere in
between, depends on the surface roughness of the feature in comparison to the wavelength of
the incoming radiation. If the wavelengths are much smaller than the surface variations or the
particle sizes that make up the surface, diffuse reflection will dominate. For example, fine
grained sand would appear fairly smooth to long wavelength microwaves but would appear
quite rough to the visible wavelengths.
Let's take a look at a couple of examples of targets at the Earth's surface and how energy at
the visible and infrared wavelengths interacts with them.

A chemical compound in leaves called chlorophyll strongly
absorbs radiation in the red and blue wavelengths but
reflects green wavelengths. Leaves appear "greenest“ to us
in the summer, when chlorophyll content is at its maximum.
In autumn, there is less chlorophyll in the leaves, so there is
less absorption and proportionately more reflection of the
red wavelengths, making the leaves appear red or yellow
(yellow is a combination of red and green wavelengths).
Leaves
Water: Longer wavelength visible and near infrared radiation is absorbed more by water than
shorter visible wavelengths. Thus water typically looks blue or blue-green due to stronger
reflectance at these shorter wavelengths, and darker if viewed at red or near infrared
wavelengths.

If there is suspended sediment present in the upper layers of the water body, then this will
allow better reflectivity and a brighter appearance of the water. The apparent color of the
water will show a slight shift to longer. wavelengths. Suspended sediment (S) can be easily
confused with shallow (but clear) water, since these two phenomena appear very similar.
Chlorophyll in algae absorbs more of the blue wavelengths and reflects the green, making the
water appear more green in colour when algae is present. The topography of the water surface
(rough, smooth, floating materials, etc.) can also lead to complications for water-related
interpretation due to potential problems of specular reflection and the influences on colour
and brightness.
We can see from these examples that, depending on
the complex make-up of the target that is being
looked at, and the wavelengths of radiation involved,
we can observe very different responses to the
mechanisms of absorption, transmission, and
reflection.

By comparing the response patterns of different features we may be able to distinguish
between them, where we might not be able to, if we only compared them at one wavelength.
For example, water and vegetation may reflect somewhat similarly in the visible wavelengths
but are almost always separable in the infrared. Spectral response can be quite variable, even
for the same target type, and can also vary with time (e.g. "green-ness" of leaves) and
location. Knowing where to "look" spectrally and understanding the factors which influence
the spectral response of the features of interest are critical to correctly interpreting the
interaction of electromagnetic radiation with the surface.
1.6 Passive vs. Active Sensing
So far, throughout this chapter, we have made various references to the sun as a source of
energy or radiation. The sun provides a very convenient source of energy for remote sensing.
The sun's energy is either reflected, as it is for visible wavelengths, or absorbed and then
reemitted, as it is for thermal infrared wavelengths. Remote sensing systems which measure
energy that is naturally available are called passive sensors.

Passive sensors can only be used to detect energy when the
naturally occurring energy is available. For all reflected
energy, this can only take place during the time when the sun
is illuminating the Earth. There is no reflected energy
available from the sun at night. Energy that is naturally
emitted (such as thermal infrared) can be detected day or
night, as long as the amount of energy is large enough to be
recorded
Active sensors, on the other hand, provide their own energy source for illumination. The
sensor emits radiation which is directed toward the target to be investigated. The radiation
reflected from that target is detected and measured by the sensor. Advantages for active sensors
include the ability to obtain measurements anytime, regardless of the time of day or season.
Active sensors can be used for examining wavelengths that are not sufficiently provided by the
sun, such as microwaves, or to better control the way a target is illuminated. However, active
systems require the generation of a fairly large amount of energy to adequately illuminate
targets. Some examples of active sensors are a laser fluoro sensor and a Synthetic Aperture
Radar (SAR).

1.7 Characteristics of Images
Before we go on to the next chapter, which looks in more detail at sensors and their
characteristics, we need to define and understand a few fundamental terms and concepts
associated with remote sensing images.
Electromagnetic energy may be detected either
photographically or electronically. The photographic
process uses chemical reactions on the surface of light-
sensitive film to detect and record energy variations. It
is important to distinguish between the terms images
and photographs in remote sensing. An image refers to
any pictorial representation, regardless of what
wavelengths or remote sensing device has been used to
detect and record the electromagnetic energy.
A photograph refers specifically to images that have
been detected as well as recorded on photographic
film.

Photos are normally recorded over the wavelength range from 0.3 µm to 0.9 µm - the
visible and reflected infrared. Based on these definitions, we can say that all photographs
are images, but not all images are photographs. Therefore, unless we are talking
specifically about an image recorded photographically, we use the term image. A
photograph could also be represented and displayed in a digital format by subdividing the
image into small equal-sized and shaped areas, called picture elements or pixels, and
representing the brightness of each area with a numeric value or digital number. Indeed,
that is exactly what has been done to the photo to the left. In fact, using the definitions we
have just discussed, this is actually a digital image of the original photograph! The
photograph was scanned and subdivided into pixels with each pixel assigned a digital
number representing its relative brightness.
The computer displays each digital value as different brightness levels. Sensors that
record electromagnetic energy, electronically record the energy as an array of
numbers in digital format right from the start.

These two different ways of representing and displaying remote sensing data, either
pictorially or digitally, are interchangeable as they convey the same information (although
some detail may be lost when converting back and forth).
In previous sections we described the visible portion of the spectrum and the concept of
colours. We see colour because our eyes detect the entire visible range of wavelengths and our
brains process the information into separate colours. Can you imagine what the world would
look like if we could only see very narrow ranges of wavelengths or colours? That is how
many sensors work. The information from a narrow wavelength range is gathered and stored
in a channel, also sometimes referred to as a band. We can combine and display channels of
information digitally using the three primary colours (blue, green, and red). The data from
each channel is represented as one of the primary colours and, depending on the relative
brightness (i.e. the digital value) of each pixel in each channel, the primary colours combine in
different proportions to represent different colours.

When we use this method to display a single channel or range of wavelengths, we are actually
displaying that channel through all three primary colours. Because the brightness level of each
pixel is the same for each primary colour, they combine to form a black and white image,
showing various shades of gray from black to white. When we display more than one channel
each as a different primary colour, then the brightness levels may be different for each
channel/primary colour combination and they will combine to form a colour image.

1.1 Did You Know?
Of our five senses (sight, hearing, taste, smell, touch), three may be considered forms of
"remote sensing", where the source of information is at some distance. The other two rely
on direct contact with the source of information - which are they?
1.2 Did You Know?
...that remote sensing, in its broadest definition, includes
ultrasounds, satellite weather maps, speed radar, graduation
photos, and sonar - both for ships and for bats!. Hospitals use
imaging technology, including CAT scans, magnetic
resonance imaging (3-D imaging of soft tissue), and x-rays
for examining our bodies. These are all examples of non-
intrusive remote sensing methods.
you can use an oscilloscope, a special electronic device which displays waves similar to the
electromagnetic radiation waves you have seen here, to look at the wavelength and frequency
patterns of your voice.

...that the concept of wavelength and frequency is an important
principle behind something called the Doppler Shift, which
explains how sound and light waves are perceived to be
compressed or expanded if the object producing them is moving
relative to the sensor.
As a train or race car advances towards us, our ears tend to hear progressively lower sounds
or frequencies (shorter wavelengths) until it reaches us, the original frequency of the object
when it is broadside, then even lower frequencies as it moves further away. This same
principle (applied to light) is used by astronomers to see how quickly stars are moving away
from us (the Red shift).

1.3 Did You Know?
Hue and saturation are independent characteristics of colour. Hue refers to the wavelength
of light, which we commonly call "colour", while saturation indicates how pure the colour is, or
how much white is mixed in with it. For instance, "pink" can be considered a less saturated
version of "red.

1.4 Did You Know?
"...sorry, no pot of gold at the end of this rainbow..."
...water droplets act as tiny, individual prisms. When sunlight passes through them, the
constituent wavelengths are bent in varying amounts according to wavelength. Individual
colours in the sunlight are made visible and a rainbow is the result, with shorter wavelengths
(violet, blue) in the inner part of the arc, and longer wavelengths (orange, red) along the outer
arc.
...if scattering of radiation in the atmosphere did not take place, then shadows would appear
as jet black instead of being various degrees of darkness.

Scattering causes the atmosphere to have its own brightness (from the light scattered by
particles in the path of sunlight) which helps to illuminate the objects in the shadows.
1.5 Did You Know?
"...now, here's something to 'reflect' on..."
... the colours we perceive are a combination of these radiation interactions (absorption,
transmission, reflection), and represent the wavelengths being reflected. If all visible
wavelengths are reflected from an object, it will appear white, while an object absorbing all
visible wavelengths will appear colourless, or black.

1.6 Did You Know?
"...say 'Cheese'!...“...a camera provides an excellent example of both passive and active sensors.
During a bright sunny day, enough sunlight is illuminating the targets and then reflecting
toward the camera lens, that the camera simply records the radiation provided (passive mode).
On a cloudy day or inside a room, there is often not enough sunlight for the camera to record
the targets adequately. Instead, it uses its own energy source - a flash - to illuminate the targets
and record the radiation reflected from them (active mode)
... radar used by police to measure the speed of traveling
vehicles is a use of active remote sensing. The radar device is
pointed at a vehicle, pulses of radiation are emitted, and the
reflection of that radiation from the vehicle is detected and
timed. The speed of the vehicle is determined by calculating
time delays between the repeated emissions and reception of the
pulses. This can be calculated very accurately because the speed
of the radiation is moving much, much faster than most
vehicles...unless you're driving at the speed of light!

1.7 Did You Know?
Photographic film has the clear advantage of recording
extremely fine spatial detail, since individual silver halide
molecules can record light sensitivity differently than their
neighbouring molecules. But when it comes to spectral and
radiometric qualities, digital sensors outperform film, by being
able to use extremely fine spectral bands (for spectral
'fingerprinting' of targets), and recording up to many thousands
of levels of brightness .

2.1 On the Ground, In the Air, In Space
In Chapter 1 we learned some of the fundamental concepts required to understand the process
that encompasses remote sensing. We covered in some detail the first three components of this
process: the energy source, interaction of energy with the atmosphere, and interaction of energy
with the surface. We touched briefly on the fourth component - recording of energy by the
sensor - when we discussed passive vs active sensors and characteristics of images.
In this chapter, we will take a closer look at this
component of the remote sensing process by examining
in greater detail, the characteristics of remote sensing
platforms and sensors and the data they collect. We will
also touch briefly on how those data are processed once
they have been recorded by the sensor.

In order for a sensor to collect and record energy reflected or emitted from a target or surface, it
must reside on a stable platform removed from the target or surface being observed. Platforms
for remote sensors may be situated on the ground, on an aircraft or balloon (or some other
platform within the Earth's atmosphere), or on a spacecraft or satellite outside of the Earth's
atmosphere.
Ground-based sensors are often used to record detailed
information about the surface which is compared with information
collected from aircraft or satellite sensors. In some cases this can be
used to better characterize the target which is being imaged by
these other sensors, making it possible to better understand the
information in the imagery.
Sensors may be placed on a ladder, scaffolding, tall building,
cherry-picker, crane, etc. Aerial platforms are primarily stable
wing aircraft, although helicopters are occasionally
used. Aircraft are often used to collect very detailed images and
facilitate the collection of data over virtually any portion of the
Earth's surface at any time.

In space, remote sensing is sometimes conducted from the
space shuttle or, more commonly, from satellites. Satellites are
objects which revolve around another object - in this case, the
Earth.
For example, the moon is a natural satellite, whereas man-made satellites include those
platforms launched for remote sensing, communication, and telemetry (location and navigation)
purposes. Because of their orbits, satellites permit repetitive coverage of the Earth's surface on a
continuing basis. Cost is often a significant factor in choosing among the various platform
options.
In space, remote sensing is sometimes conducted from the
space shuttle or, more commonly, from satellites. Satellites
are objects which revolve around another object - in this case,
the Earth.
For example, the moon is a natural satellite, whereas man-
made satellites include those platforms launched for remote
sensing, communication, and telemetry (location and
navigation) purposes.

Because of their orbits, satellites permit repetitive coverage of
the Earth's surface on a continuing basis. Cost is often a
significant factor in choosing among the various platform
options.
2.2 Satellite Characteristics: Orbits and Swaths
We learned in the previous section that remote sensing instruments can be placed on a
variety of platforms to view and image targets. Although ground-based and aircraft platforms
may be used, satellites provide a great deal of the remote sensing imagery commonly used
today. Satellites have several unique characteristics which make them particularly useful for
remote sensing of the Earth's surface.

The path followed by a satellite is referred to as its orbit. Satellite orbits are matched to the
capability and objective of the sensor(s) they carry. Orbit selection can vary in terms of altitude
(their height above the Earth's surface) and their orientation and rotation relative to the Earth.
This allows the satellites to observe and collect information
continuously over specific areas. Weather and communications
satellites commonly have these types of orbits. Due to their
high altitude, some geostationary weather satellites can
monitor weather and cloud patterns covering an entire
hemisphere of the Earth.
Geostationary Satellites at very high altitudes, which view the same portion of the Earth's
surface at all times have geostationary orbits. These geostationary satellites, at altitudes of
approximately 36,000 kilometres, revolve at speeds which match the rotation of the Earth so
they seem
a) Geostationary satellites

Near polar platforms are designed to follow an orbit (basically north-south) which, in
conjunction with the Earth's rotation (west-east), allows them to cover most of the Earth's
surface over a certain period of time.
These are nearpolar orbits, so named for the inclination of
the orbit relative to a line running between the North and
South poles. Many of these satellite orbits are also sun-
synchronous such that they cover each area of the world at
a constant local time of day called local sun time. At any
given latitude, the position of the sun in the sky as the
satellite passes overhead will be the same within the same
season.
b) Nearpolar orbits
This ensures consistent illumination conditions when
acquiring images in a specific season over successive years,
or over a particular area over a series of days.

This is an important factor for monitoring changes between images or for mosaicking adjacent
images together, as they do not have to be corrected for different illumination conditions.
These are called ascending and descending passes, respectively. If
the orbit is also sunsynchronous, the ascending pass is most likely
on the shadowed side of the Earth while the descending pass is on
the sunlit side. Sensors recording reflected solar energy only image
the surface on a descending pass, when solar illumination is
available. Active sensors which provide their own illumination or
passive sensors that record emitted (e.g. thermal) radiation can also
image the surface on ascending passes
Most of the remote sensing satellite platforms today are in near-polar orbits, which means that
the satellite travels northwards on one side of the Earth and then toward the southern pole on
the second half of its orbit.

However, as seen from the Earth, it seems that the satellite is
shifting westward because the Earth is rotating (from west to
east) beneath it. This apparent movement allows the satellite
swath to cover a new area with each consecutive pass. The
satellite's orbit and the rotation of the Earth work together to
allow complete coverage of the Earth's surface, after it has
completed one complete cycle of orbits.
As a satellite revolves around the Earth, the sensor "sees" a certain portion of the Earth's
surface. The area imaged on the surface, is referred to as the swath. Imaging swaths for
spaceborne sensors generally vary between tens and hundreds of kilometres wide. As the
satellite orbits the Earth from pole to pole, its east-west position wouldn't change if the Earth
didn't rotate.

If we start with any randomly selected pass in a satellite's
orbit, an orbit cycle will be completed when the satellite
retraces its path, passing over the same point on the Earth's
surface directly below the satellite (called the nadir point) for
a second time. The exact length of time of the orbital cycle
will vary with each satellite. The interval of time required for
the satellite to complete its orbit cycle is not the same as the
"revisit period". Using steerable sensors, an satellite-borne
instrument can view an area (off-nadir) before and after the
orbit passes over a target, thus making the 'revisit' time less
than the orbit cycle time.

The revisit period is an important consideration for a number of
monitoring applications, especially when frequent imaging is
required (for example, to monitor the spread of an oil spill, or the
extent of flooding). In near-polar orbits, areas at high latitudes
will be imaged more frequently than the equatorial zone due to the
increasing overlap in adjacent swaths as the orbit paths come
closer together near the poles.
2.3 Spatial Resolution, Pixel Size, and Scale
For some remote sensing instruments, the distance between the target being imaged and the
platform, plays a large role in determining the detail of information obtained and the total area
imaged by the sensor. Sensors onboard platforms far away from their targets, typically view a
larger area, but cannot provide great detail. Compare what an astronaut onboard the space
shuttle sees of the Earth to what you can see from an airplane. The astronaut might see your
whole province or country in one glance, but couldn't distinguish individual houses.

Flying over a city or town, you would be able to see individual buildings and cars, but you
would be viewing a much smaller area than the astronaut. There is a similar difference between
satellite images and airphotos
The detail discernible in an image is dependent on the spatial
resolution of the sensor and refers to the size of the smallest
possible feature that can be detected. Spatial resolution of
passive sensors (we will look at the special case of active
microwave sensors later) depends primarily on their
Instantaneous Field of View (IFOV).
The IFOV is the angular cone of visibility of the sensor (A)
and determines the area on the Earth's surface which is "seen"
from a given altitude at one particular moment in time (B).
The size of the area viewed is determined by multiplying the
IFOV by the distance from the ground to the sensor (C).

This area on the ground is called the resolution cell and determines a sensor's maximum
spatial resolution. For a homogeneous feature to be detected, its size generally has to be equal
to or larger than the resolution cell. If the feature is smaller than this, it may not be detectable
as the average brightness of all features in that resolution cell will be recorded. However,
smaller features may sometimes be detectable if their reflectance dominates within a articular
resolution cell allowing sub-pixel or resolution cell detection.
As we mentioned in Chapter 1, most remote sensing images are composed of a matrix of
picture elements, or pixels, which are the smallest units of an image.

Image pixels are normally square and represent a certain area on an image. It is important to
distinguish between pixel size and spatial resolution - they are not interchangeable. If a sensor
has a spatial resolution of 20 metres and an image from that sensor is displayed at full
resolution, each pixel represents an area of 20m x 20m on the ground. In this case the pixel size
and resolution are the same. However, it is possible to display an image with a pixel size
different than the resolution. Many posters of satellite images of the Earth have their pixels
averaged to represent larger areas, although the original spatial resolution of the sensor that
collected the imagery remains the same.
Images where only large features are visible are said to have coarse or low resolution. In
fine or high resolution images, small objects can be detected. Military sensors for example,
are designed to view as much detail as possible, and therefore have very fine resolution.
Commercial satellites provide imagery with resolutions varying from a few metres to several
kilometres. Generally speaking, the finer the resolution, the less total ground area can be
seen. The ratio of distance on an image or map, to actual ground distance is referred to as
scale.

If you had a map with a scale of 1:100,000, an object of 1cm length on the map would
actually be an object 100,000cm (1km) long on the ground. Maps or images with small "map-
to-ground ratios" are referred to as small scale (e.g. 1:100,000), and those with larger ratios
(e.g.1:5,000) are called large scale.
2.4 Spectral Resolution
In Chapter 1, we learned about spectral response and
spectral emissivity curves which characterize the
reflectance and/or emittance of a feature or target over a
variety of wavelengths. Different classes of features and
details in an image can often be distinguished by
comparing their responses over distinct wavelength
ranges. Broad classes, such as water and vegetation, can
usually be separated using very broad wavelength ranges -
the visible and near infrared.

Other more specific classes, such as different rock types, may not be easily distinguishable
using either of these broad wavelength ranges and would require comparison at much finer
wavelength ranges to separate them. Thus, we would require a sensor with higher spectral
resolution. Spectral resolution describes the ability of a sensor to define fine wavelength
intervals. The finer the spectral resolution, the narrower the wavelength range for a particular
channel or band.
Black and white film records wavelengths extending over
much, or all of the visible portion of the electromagnetic
spectrum. Its spectral resolution is fairly coarse, as the
various wavelengths of the visible spectrum are not
individually distinguished and the overall reflectance in
the entire visible portion is recorded. Colour film is also
sensitive to the reflected energy over the visible portion
of the spectrum, but has higher spectral resolution, as it is
individually sensitive to the reflected energy at the blue,
green, and red wavelengths of the spectrum..

Thus, it can represent features of various colours based on their reflectance in each of these
distinct wavelength ranges. Many remote sensing systems record energy over several separate
wavelength ranges at various spectral resolutions. These are referred to as multi-spectral
sensors and will be described in some detail in following sections. Advanced multi-spectral
sensors called hyperspectral sensors, detect hundreds of very narrow spectral bands
throughout the visible, near-infrared, and mid-infrared portions of the electromagnetic
spectrum. Their very high spectral resolution facilitates fine discrimination between different
targets based on their spectral response in each of the narrow bands.
2.5 Radiometric Resolution
While the arrangement of pixels describes the spatial structure of an image, the radiometric
characteristics describe the actual information content in an image. Every time an image is
acquired on film or by a sensor, its sensitivity to the magnitude of the electromagnetic
energy determines the radiometric resolution. The radiometric resolution of an imaging
system describes its ability to discriminate very slight differences in energy The finer the
radiometric resolution of a sensor, the more sensitive it is to detecting small differences in
reflected or emitted energy

Imagery data are represented by positive digital numbers
which vary from 0 to (one less than) a selected power of 2.
This range corresponds to the number of bits used for coding
numbers in binary format. Each bit records an exponent of
power 2 (e.g. 1 bit=2 1=2). The maximum number of
brightness levels available depends on the number of bits used
in representing the energy recorded. Thus, if a sensor used 8
bits to record the data, there would be 28=256 digital values
available, ranging from 0 to 255.
However, if only 4 bits were used, then only 24=16 values ranging from 0 to 15 would be
available. Thus, the radiometric resolution would be much less. Image data are generally
displayed in a range of grey tones, with black representing a digital number of 0 and white
representing the maximum value (for example, 255 in 8-bit data). By comparing a 2-bit
image with an 8-bit image, we can see that there is a large difference in the level of detail
discernible depending on their radiometric resolutions.

2.6 Temporal Resolution
In addition to spatial, spectral, and radiometric resolution, the concept of temporal resolution
is also important to consider in a remote sensing system. We alluded to this idea in section 2.2
when we discussed the concept of revisit period, which refers to the length of time it takes for
a satellite to complete one entire orbit cycle.
The revisit period of a satellite sensor is usually several days.
Therefore the absolute temporal resolution of a remote sensing
system to image the exact same area at the same viewing angle a
second time is equal to this period. However, because of some
degree of overlap in the imaging swaths of adjacent orbits for most
satellites and the increase in this overlap with increasing latitude,
some areas of the Earth tend to be re-imaged more frequently.
Also, some satellite systems are able to point their sensors to image the same area between
different satellite passes separated by periods from one to five days. Thus, the actual temporal
resolution of a sensor depends on a variety of factors, including the satellite/sensor
capabilities, the swath overlap, and latitude.

The ability to collect imagery of the same area of the Earth's surface at different periods of
time is one of the most important elements for applying remote sensing data. Spectral
characteristics of features may change over time and these changes can be detected by
collecting and comparing multi-temporal imagery.
For example, during the growing season, most species of vegetation are in a continual state
of change and our ability to monitor those subtle changes using remote sensing is dependent
on when and how frequently we collect imagery. By imaging on a continuing basis at
different times we are able to monitor the changes that take place on the Earth's surface,
whether they are naturally occurring (such as changes in natural vegetation cover or
flooding) or induced by humans (such as urban development or deforestation). The time
factor in imaging is important when:
• persistent clouds offer limited clear views of the Earth's surface (often in the tropics)
• short-lived phenomena (floods, oil slicks, etc.) need to be imaged
• multi-temporal comparisons are required (e.g. the spread of a forest disease from one year
to the next)
• the changing appearance of a feature over time can be used to distinguish it from near
similar features (wheat / maize)

2.7 Cameras and Aerial Photography
Cameras and their use for aerial photography are the simplest and oldest of sensors used for
remote sensing of the Earth's surface. Cameras are framing systems which acquire a near-
instantaneous "snapshot" of an area (A), of the surface. Camera systems are passive optical
sensors that use a lens (B) (or system of lenses collectively referred to as the optics) to form an
image at the focal plane (C), the plane at which an image is sharply defined.
Photographic films are sensitive to light from 0.3 µm to 0.9 µm in
wavelength covering the ultraviolet (UV), visible, and near-infrared
(NIR). Panchromatic films are sensitive to the UV and the visible
portions of the spectrum. Panchromatic film produces black and white
images and is the most common type of film used for aerial photography.
UV photography also uses panchromatic film, but a filter is used with
the camera to absorb and block the visible energy from reaching the
film.

As a result, only the UV reflectance from targets is recorded. UV photography is not widely
used, because of the atmospheric scattering and absorption that occurs in this region of the
spectrum. Black and white infrared photography uses film sensitive to the entire 0.3 to 0.9 µm
wavelength range and is useful for detecting differences in vegetation cover, due to its
sensitivity to IR reflectance.
Colour and false colour (or colour infrared, CIR) photography involves the use of a three layer
film with each layer sensitive to different ranges of light. For a normal colour photograph, the
layers are sensitive to blue, green, and red light - the same as our eyes.

These photos appear to us the same way that our eyes see the environment, as the colours
resemble those which would appear to us as "normal" (i.e. trees appear green, etc.). In colour
infrared (CIR) photography, the three emulsion layers are sensitive to green, red, and the
photographic portion of near-infrared radiation, which are processed to appear as blue, green,
and red, respectively. In a false colour photograph, targets with high near-infrared reflectance
appear red, those with a high red reflectance appear green, and those with a high green
reflectance appear blue, thus giving us a "false" presentation of the targets relative to the colour
we normally perceive them to be.
Cameras can be used on a variety of platforms including ground-based stages, helicopters,
aircraft, and spacecraft. Very detailed photographs taken from aircraft are useful for many
applications where identification of detail or small targets is required. The ground coverage of
a photo depends on several factors, including the focal length of the lens, the platform
altitude, and the format and size of the film. The focal length effectively controls the angular
field of view of the lens (similar to the concept of instantaneous field of view discussed in
section 2.3) and determines the area "seen" by the camera. Typical focal lengths used are
90mm, 210mm, and most commonly, 152mm. The longer the focal length, the smaller the
area covered on the ground, but with greater detail (i.e. larger scale).

The area covered also depends on the altitude of the platform. At high altitudes, a camera will
"see" a larger area on the ground than at lower altitudes, but with reduced detail (i.e. smaller
scale). Aerial photos can provide fine detail down to spatial resolutions of less than 50 cm. A
photo's exact spatial resolution varies as a complex function of many factors which vary with
each acquisition of data.
Most aerial photographs are classified as either oblique
or vertical, depending on the orientation of the camera
relative to the ground during acquisition. Oblique aerial
photographs are taken with the camera pointed to the
side of the aircraft. High oblique photographs usually
include the horizon while low oblique photographs do
not. Oblique photographs can be useful for covering
very large areas in a single image and for depicting
terrain relief and scale.
However, they are not widely used for mapping as distortions in scale from the foreground
to the background preclude easy measurements of distance, area, and elevation.

Vertical photographs taken with a single-lens frame camera is the most common use of aerial
photography for remote sensing and mapping purposes. These cameras are specifically built for
capturing a rapid sequence of photographs while limiting geometric distortion. They are often
linked with navigation systems onboard the aircraft platform, to allow for accurate geographic
coordinates to be instantly assigned to each photograph. Most camera systems also include
mechanisms which compensate for the effect of the aircraft motion relative to the ground, in
order to limit distortion as much as possible.
When obtaining vertical aerial photographs, the aircraft
normally flies in a series of lines, each called a flight line.
Photos are taken in rapid succession looking straight down
at the ground, often with a 50-60 percent overlap (A)
between successive photos.
The overlap ensures total coverage along a flight line and
also facilitates stereoscopic viewing.

Successive photo pairs display the overlap region from different perspectives and can be
viewed through a device called a stereoscope to see a three-dimensional view of the area,
called a stereo model. Many applications of aerial photography use stereoscopic coverage and
stereo viewing.
Aerial photographs are most useful when fine spatial detail is more critical than spectral
information, as their spectral resolution is generally coarse when compared to data captured
with electronic sensing devices. The geometry of vertical photographs is well understood and
it is possible to make very accurate measurements from them, for a variety of different
applications (geology, forestry, mapping, etc.).
The science of making measurements from photographs is called photogrammetry and has
been performed extensively since the very beginnings of aerial photography. Photos are most
often interpreted manually by a human analyst (often viewed stereoscopically). They can also
be scanned to create a digital image and then analyzed in a digital computer environment.

Multiband photography uses multi-lens systems with different film-filter combinations to
acquire photos simultaneously in a number of different spectral ranges. The advantage of these
types of cameras is their ability to record reflected energy separately in discrete wavelength
ranges, thus providing potentially better separation and identification of various features.
However, simultaneous analysis of these multiple photographs can be problematic.
Digital cameras, which record electromagnetic radiation electronically, differ significantly
from their counterparts which use film. Instead of using film, digital cameras use a gridded
array of silicon coated CCDs (charge-coupled devices) that individually respond to
electromagnetic radiation. Energy reaching the surface of the CCDs causes the generation of
an electronic charge which is proportional in magnitude to the "brightness" of the ground
area. A digital number for each spectral band is assigned to each pixel based on the
magnitude of the electronic charge.
The digital format of the output image is amenable to digital analysis and archiving in a
computer environment, as well as output as a hardcopy product similar to regular photos.
Digital cameras also provide quicker turn-around for acquisition and retrieval of data and
allow greater control of the spectral resolution.

Although parameters vary, digital imaging systems are capable of collecting data with a spatial
resolution of 0.3m, and with a spectral resolution of 0.012 mm to 0.3 mm. The size of the pixel
arrays varies between systems, but typically ranges between 512 x 512 to 2048 x 2048.
2.8 Multispectral Scanning
Many electronic (as opposed to photographic) remote sensors acquire data using scanning
systems, which employ a sensor with a narrow field of view (i.e. IFOV) that sweeps over the
terrain to build up and produce a two-dimensional image of the surface. Scanning systems
can be used on both aircraft and satellite platforms and have essentially the same operating
principles.
A scanning system used to collect data over a variety of different wavelength ranges is called
a multispectral scanner (MSS), and is the most commonly used scanning
system. There are two main modes or methods of scanning employed to acquire multispectral
image data - across-track scanning, and along-track scanning.

They scan the Earth in a series of lines. The lines are oriented perpendicular to the direction of
motion of the sensor platform (i.e. across the swath). Each line is scanned from one side of the
sensor to the other, using a rotating mirror (A). As the platform moves forward over the Earth,
successive scans build up a two-dimensional image of the Earth´s surface. The incoming
reflected or emitted radiation is separated into several spectral components that are detected
independently. The UV, visible, near-infrared, and thermal radiation are dispersed into their
constituent wavelengths. A bank of internal detectors (B), each sensitive to a specific range of
wavelengths, detects and measures the energy for each spectral band and then, as an electrical
signal, they are converted to digital data and recorded for subsequent computer processing.
The IFOV (C) of the sensor and the altitude of the platform
determine the ground resolution cell viewed (D), and thus the
spatial resolution. The angular field of view (E) is the sweep of
the mirror, measured in degrees, used to record a scan line, and
determines the width of the imaged swath (F).
a)Across-track scanners

Airborne scanners typically sweep large angles (between 90º and 120º), while satellites,
because of their higher altitude need only to sweep fairly small angles (10-20º) to cover a
broad region. Because the distance from the sensor to the target increases towards the edges
of the swath, the ground resolution cells also become larger and introduce geometric
distortions to the images. Also, the length of time the IFOV "sees" a ground resolution cell as
the rotating mirror scans (called the dwell time), is generally quite short and influences the
design of the spatial, spectral, and radiometric resolution of the sensor.
They also use the forward motion of the platform to record successive scan lines and build
up a two-dimensional image, perpendicular to the flight direction.
b) Along-track scanners
However, instead of a scanning mirror, they use a linear array of
detectors (A) located at the focal plane of the image (B) formed by
lens systems (C), which are "pushed" along in the flight track
direction (i.e. along track). These systems are also referred to as
pushbroom scanners, as the motion of the detector array is
analogous to the bristles of a broom being pushed along a floor.

Each individual detector measures the energy for a single ground resolution cell (D) and thus
the size and IFOV of the detectors determines the spatial resolution of the system. A separate
linear array is required to measure each spectral band or channel. For each scan line, the energy
detected by each detector of each linear array is sampled electronically and digitally recorded.
Along-track scanners with linear arrays have several advantages over across-track mirror
scanners. The array of detectors combined with the pushbroom motion allows each
detector to "see" and measure the energy from each ground resolution cell for a longer
period of time (dwell time). This allows more energy to be detected and improves the
radiometric resolution. The increased dwell time also facilitates smaller IFOVs and
narrower bandwidths for each detector. Thus, finer spatial and spectral resolution can be
achieved without impacting radiometric resolution. Because detectors are usually solid-
state microelectronic devices, they are generally smaller, lighter, require less power, and are
more reliable and last longer because they have no moving parts. On the other hand, cross-
calibrating thousands of detectors to achieve uniform sensitivity across the array is
necessary and complicated.

Regardless of whether the scanning system used is either of these two types, it has several
advantages over photographic systems. The spectral range of photographic systems is
restricted to the visible and near-infrared regions while MSS systems can extend this range
into the thermal infrared. They are also capable of much higher spectral resolution than
photographic systems. Multi-band or multispectral photographic systems use separate lens
systems to acquire each spectral band.
This may cause problems in ensuring that the different bands are comparable both spatially and
radiometrically and with registration of the multiple images. MSS systems acquire all spectral
bands simultaneously through the same optical system to alleviate these problems.
Photographic systems record the energy detected by means of a photochemical process which
is difficult to measure and to make consistent.
Because MSS data are recorded electronically, it is easier to determine the specific amount of
energy measured, and they can record over a greater range of values in a digital format.
Photographic systems require a continuous supply of film and processing on the ground after
the photos have been taken. The digital recording in MSS systems facilitates transmission of
data to receiving stations on the ground and immediate processing of data in a computer
environment.

2.9 Thermal Imaging
Many multispectral (MSS) systems sense radiation in the thermal infrared as well as the visible
and reflected infrared portions of the spectrum. However, remote sensing of energy emitted
from the Earth's surface in the thermal infrared (3 µm to 15 µm) is different than the sensing of
reflected energy.
Thermal sensors use photo detectors sensitive to the direct
contact of photons on their surface, to detect emitted thermal
radiation. The detectors are cooled to temperatures close to
absolute zero in order to limit their own thermal emissions.
Thermal sensors essentially measure the surface temperature
and thermal properties of targets.
Thermal imagers are typically across-track scanners (like those described in the previous
section) that detect emitted radiation in only the thermal portion of the spectrum. Thermal
sensors employ one or more internal temperature references for comparison with the detected
radiation, so they can be related to absolute radiant temperature.

The data are generally recorded on film and/or magnetic tape and the temperature resolution of
current sensors can reach 0.1 °C. For analysis, an image of relative radiant temperatures
(a thermogram) is depicted in grey levels, with warmer temperatures shown in light tones,
and cooler temperatures in dark tones.
Imagery which portrays relative temperature differences in their
relative spatial locations are sufficient for most applications.
Absolute temperature measurements may be calculated but
require accurate calibration and measurement of the temperature
references and detailed knowledge of the thermal properties of
the target, geometric distortions, and radiometric effects.
Because of the relatively long wavelength of thermal radiation (compared to visible radiation),
atmospheric scattering is minimal. However, absorption by atmospheric gases normally
restricts thermal sensing to two specific regions 3 to 5 µm and 8 to 14 µm. Because energy
decreases as the wavelength increases, thermal sensors generally have large IFOVs to
ensure that enough energy reaches the detector in order to make a reliable measurement.

Therefore the spatial resolution of thermal sensors is usually fairly coarse, relative to the
spatial resolution possible in the visible and reflected infrared. Thermal imagery can be
acquired during the day or night (because the radiation is emitted not reflected) and is used for
a variety of applications such as military reconnaissance, disaster management (forest fire
mapping), and heat loss monitoring

2.10 Geometric Distortion in Imagery
Any remote sensing image, regardless of whether it is acquired by a multispectral scanner on
board a satellite, a photographic system in an aircraft, or any other platform/sensor
combination, will have various geometric distortions. This problem is inherent in remote
sensing, as we attempt to accurately represent the three-dimensional surface of the Earth as
a two-dimensional image. All remote sensing images are subject to some form of geometric
distortions, depending on the manner in which the data are acquired. These errors may be
due to a variety of factors, including one or more of the following, to name only a few:
 the perspective of the sensor optics
 the motion of the scanning system,
 the motion and (in)stability of the platform,
 the platform altitude, attitude, and velocity,
 the terrain relief, and
 the curvature and rotation of the Earth.

Framing systems, such as cameras used for aerial photography, provide an instantaneous
"snapshot" view of the Earth from directly overhead. The primary geometric distortion in
vertical aerial photographs is due to relief displacement. Objects directly below the centre of
the camera lens (i.e. at the nadir) will have only their tops visible, while all other objects will
appear to lean away from the centre of the photo such that their tops and sides are visible. If
the objects are tall or are far away from the centre of the photo, the distortion and positional
error will be larger.
The geometry of along-track scanner imagery is similar to
that of an aerial photograph for each scan line as each
detector essentially takes a "snapshot" of each ground
resolution cell. Geometric variations between lines are
caused by random variations in platform altitude and
attitude along the direction of flight.
Images from across-track scanning systems exhibit two main types of geometric distortion.
They too exhibit relief displacement (A), similar to aerial photographs, but in only one
direction parallel to the direction of scan.

There is no displacement directly below the sensor, at nadir. As the sensor scans across the
swath, the top and side of objects are imaged and appear to lean away from the nadir point in
each scan line. Again, the displacement increases, moving towards the edges of the swath.
Another distortion (B) occurs due to the rotation of the scanning optics. As the sensor scans
across each line, the distance from the sensor to the ground increases further away from the
centre of the swath. Although the scanning mirror rotates at a constant speed, the IFOV of the
sensor moves faster (relative to the ground) and scans a larger area as it moves closer to the
edges. This effect results in the compression of image features at points away from the nadir
and is called tangential scale distortion.
All images are susceptible to geometric distortions caused by variations in platform stability
including changes in their speed, altitude, and attitude (angular orientation with respect to the
ground) during data acquisition. These effects are most pronounced when using aircraft
platforms and are alleviated to a large degree with the use of satellite platforms, as their
orbits are relatively stable, particularly in relation to their distance from the Earth. However,
the eastward rotation of the Earth, during a satellite orbit causes the sweep of scanning
systems to cover an area slightly to the west of each previous scan.

The resultant imagery is thus skewed across the image. This is known as skew distortion and
is common in imagery obtained from satellite multispectral scanners.
The sources of geometric distortion and positional error vary with each specific situation,
but are inherent in remote sensing imagery. In most instances, we may be able to remove, or
at least reduce these errors but they must be taken into account in each instance before
attempting to make measurements or extract further information.
Now that we have learned about some of the general characteristics of platforms and
sensors, in the next sections we will look at some specific sensors (primarily satellite
systems) operating in the visible and infrared portions of the spectrum.

2.11 Weather Satellites/Sensors
Weather monitoring and forecasting was one of the first civilian (as opposed to military)
applications of satellite remote sensing, dating back to the first true weather satellite, TIROS-1
(Television and Infrared Observation Satellite - 1), launched in 1960 by the United States.
Several other weather satellites were launched over the next five
years, in near-polar orbits, providing repetitive coverage of
global weather patterns. In 1966, NASA (the U.S. National
Aeronautics and Space Administration) launched the
geostationary Applications Technology Satellite (ATS-1) which
provided hemispheric images of the Earth's surface and cloud
cover every half hour.
For the first time, the development and movement of weather systems could be routinely
monitored. Today, several countries operate weather, or meteorological satellites to monitor
weather conditions around the globe.

Generally speaking, these satellites use sensors which have fairly coarse spatial resolution
(when compared to systems for observing land) and provide large areal coverage.
Their temporal resolutions are generally quite high, providing frequent observations of the
Earth's surface, atmospheric moisture, and cloud cover, which allows for near-continuous
monitoring of global weather conditions, and hence - forecasting. Here we review a few of
the representative satellites/sensors used for meteorological applications.
a) GOES
The GOES (Geostationary Operational Environmental
Satellite) System is the follow-up to the ATS series. They were
designed by NASA for the National Oceanic and Atmospheric
Administration (NOAA) to provide the United States National
Weather Service with frequent, small-scale imaging of the
Earth's surface and cloud cover. The GOES series of satellites
have been used extensively by meteorologists for weather
monitoring and forecasting for over 20 years.

These satellites are part of a global network of meteorological satellites spaced at
approximately 70° longitude intervals around the Earth in order to provide near-global
coverage. wo GOES satellites, placed in geostationary orbits 36000 km above
the equator, each view approximately one-third of the Earth. One is situated at 75°W longitude
and monitors North and South America and most of the Atlantic Ocean. The other is situated
at 135°W longitude and monitors North America and the Pacific Ocean basin. Together they
cover from 20°W to 165°E longitude. This GOES image covers a portion of the southeastern
United States, and the adjacent ocean areas where many severe storms originate and develop.
This image shows Hurricane Fran approaching the southeastern United States and the
Bahamas in September of 1996.
Two generations of GOES satellites have been launched, each measuring emitted and
reflected radiation from which atmospheric temperature, winds, moisture, and cloud cover can
be derived. The first generation of satellites consisted of GOES-1 (launched 1975) through
GOES-7 (launched 1992). Due to their design, these satellites were capable of viewing the
Earth only a small percentage of the time (approximately five per cent). The second
generation of satellites began with GOES-8 (launched 1994) and has numerous technological
improvements over the first series.

They provide near-continuous observation of the Earth allowing more frequent imaging (as
often as every 15 minutes). This increase in temporal resolution coupled with improvements in
the spatial and radiometric resolution of the sensors provides timelier information and
improved data quality for forecasting meteorological conditions.
The 19 channel sounder
measures emitted radiation in
18 thermal infrared bands and
reflected radiation in one visible
band. These data have a spatial
resolution of 8 km and 13-bit
radiometric resolution. Sounder
data are used for surface and
cloud-top temperatures,
multilevel moisture profiling in
the atmosphere, and ozone
distribution analysis

b) NOAAAVHRR
NOAA is also responsible for another series of satellites which are useful for meteorological,
as well as other, applications. These satellites, in sun-synchronous, near-polar orbits (830-
870 km above the Earth), are part of the Advanced TIROS series (originally dating back to
1960) and provide complementary information to the geostationary meteorological satellites
(such as GOES). Two satellites, each providing global coverage, work together to ensure that
data for any region of the Earth is no more than six hours old. One satellite crosses the
equator in the early morning from north-to-south while the other crosses in the afternoon
The primary sensor on board the NOAA satellites, used for both meteorology and small-
scale Earth observation and reconnaissance, is the Advanced Very High Resolution
Radiometer (AVHRR). The AVHRR sensor detects radiation in the visible, near and mid
infrared, and thermal infrared portions of the electromagnetic spectrum, over a swath width
of 3000 km. The accompanying table, outlines the AVHRR bands, their wavelengths and
spatial resolution (at swath nadir), and general applications of each.

AVHRR data can be acquired and formatted in four operational modes, differing in resolution
and method of transmission. Data can be transmitted directly to the ground and viewed as data
are collected, or recorded on board the satellite for later transmission and processing. The
accompanying table describes the various data formats and their characteristics.

Although AVHRR data are widely used for weather system
forecasting and analysis, the sensor is also well-suited to
observation and monitoring of land features. AVHRR has
much coarser spatial resolution than other typical land
observations sensors (discussed in the next section), but is
used extensively for monitoring regional, small-scale
phenomena, including mapping of sea surface temperature,
and natural vegetation and crop conditions.

Mosaics covering large areas can be created from several AVHRR data sets allowing small
scale analysis and mapping of broad vegetation cover.

c) Other Weather Satellites
The United States operates the DMSP (Defense Meteorological Satellite Program) series of
satellites which are also used for weather monitoring. These are near-polar orbiting satellites
whose Operational Line-scan System (OLS) sensor provides twice daily coverage with a swath
width of 3000 km at a spatial resolution of 2.7 km. It has two fairly broad wavelength bands: a
visible and near infrared band (0.4 to 1.1 µm) and a thermal infrared band (10.0 to 13.4 µm).
An interesting feature of the sensor is its ability to acquire visible band night time imagery
under very low illumination conditions. With this sensor, it is possible to collect striking
images of the Earth showing (typically) the night time lights of large urban centres.
There are several other meteorological satellites in orbit, launched and operated by other
countries, or groups of countries. These include Japan, with the GMS satellite series, and the
consortium of European communities, with the Meteosat satellites. Both are geostationary
satellites situated above the equator over Japan and Europe, respectively. Both provide
halfhourly imaging of the Earth similar to GOES. GMS has two bands: 0.5 to 0.75 µm (1.25
km resolution), and 10.5 to 12.5 µ m (5 km resolution). Meteosat has three bands: visible band
(0.4 to 1.1 µm; 2.5 km resolution), mid-IR (5.7 to 7.1 µm; 5 km resolution), and thermal IR
(10.5 to 12.5 µm; 5 km resolution)

2.12 Land Observation Satellites/Sensors
a) Landsat
Although many of the weather satellite systems (such as those
described in the previous section) are also used for monitoring the
Earth's surface, they are not optimized for detailed mapping of the
land surface. Driven by the exciting views from, and great success of
the early meteorological satellites in the 1960's, as well as from
images taken during manned spacecraft missions, the first satellite
designed specifically to monitor the Earth's surface, Landsat-1, was
launched by NASA in 1972.
Initially referred to as ERTS-1, (Earth Resources Technology Satellite), Landsat was designed
as an experiment to test the feasibility of collecting multi-spectral Earth observation data from
an unmanned satellite platform. Since that time, this highly successful program has collected an
abundance of data from around the world from several Landsat satellites. Originally managed
by NASA, responsibility for the Landsat program was transferred to NOAA in 1983. In 1985,
the program became commercialized, providing data to civilian and applications users.

Landsat's success is due to several factors, including: a combination of sensors with spectral
bands tailored to Earth observation; functional spatial resolution; and good areal coverage
(swath width and revisit period). The long lifespan of the program has provided a voluminous
archive of Earth resource data facilitating long term monitoring and historical records and
research. All Landsat satellites are placed in near-polar, sun-synchronous orbits. The first
three satellites (Landsats 1-3) are at altitudes around 900 km and have revisit periods of 18
days while the later satellites are at around 700 km and have revisit periods of 16 days. All
Landsat satellites have equator crossing times in the morning to optimize illumination
conditions.
A number of sensors have been on board the Landsat series of satellites, including the
Return Beam Vidicon (RBV) camera systems, the MultiSpectral Scanner (MSS) systems,
Thematic Mapper (TM) systems, Enhanced Thematic Mapper Plus (ETM+) systems, and
Operational Land Imager (OLI) systems. The most popular instrument in the early days of
Landsat was the MultiSpectral Scanner (MSS) and later the Thematic Mapper (TM). Each
of these sensors collected data over a swath width of 185 km, with a full scene being defined
as 185 km x 185 km. The MSS senses the electromagnetic radiation from the Earth's surface
in four spectral bands. Each band has a spatial resolution of approximately 60 x 80 meters
and a radiometric resolution of 6 bits, or 64 digital numbers.

Sensing is accomplished with a line scanning device using an oscillating mirror. Six scan lines
are collected simultaneously with each west to-east sweep of the scanning mirror. The
accompanying table outlines the spectral wavelength ranges for the MSS.

Data from both the TM and MSS sensors are used for a wide
variety of applications, including resource management,
mapping, environmental monitoring, and change detection
(e.g. monitoring forest clearcutting).

b) SPOT
SPOT (Système Pour l'Observation de la Terre) is a series of Earth observation imaging
satellites designed and launched by CNES (Centre National d'Études Spatiales) of France,
with support from Sweden and Belgium. SPOT-1 was launched in 1986, with successors
following every three or four years. All satellites are in sun synchronous, near-polar orbits at
altitudes around 830 km above the Earth, which results in orbit repetition every 26 days.
They have equator crossing times around 10:30 AM local solar time.
SPOT was designed to be a commercial provider of Earth
observation data, and was the first satellite to use along-track, or
pushbroom scanning technology.
The SPOT satellites each have twin high resolution visible (HRV) imaging systems, which
can be operated independently and simultaneously. Each HRV is capable of sensing either in
a high spatial resolution single-channel panchromatic (PLA) mode, or a coarser spatial
resolution three-channel multispectral (MLA) mode.

Each along-track scanning HRV sensor consists of four linear arrays of detectors: one 6000
element array for the panchromatic mode recording at a spatial resolution of 10 m, and one
3000 element array for each of the three multispectral bands, recording at 20 m spatial
resolution. The swath width for both modes is 60 km at nadir. The accompanying table
illustrates the spectral characteristics of the two different modes.
The viewing angle of the sensors can be adjusted to
look to either side of the satellite's vertical (nadir)
track, allowing off-nadir viewing which increases
the satellite's revisit capability.
This ability to point the sensors up to 27° from nadir, allows SPOT to view within a 950 km
swath and to revisit any location several times per week. As the sensors point away from
nadir, the swath varies from 60 to 80 km in width. This not only improves the ability to
monitor specific locations and increases the chances of obtaining cloud free scenes, but the
off-nadir viewing also provides the capability of acquiring imagery for stereoscopic coverage.

By recording the same area from two different angles, the imagery
can be viewed and analyzed as a three dimensional model, a
technique of tremendous value for terrain interpretation, mapping,
and visual terrain simulations.
This oblique viewing capability increases the revisit
frequency of equatorial regions to three days (seven times
during the 26 day orbital cycle). Areas at a latitude of 45º can
be imaged more frequently (11 times in 26 days) due to the
convergence or orbit paths towards the poles. By pointing
both HRV sensors to cover adjacent ground swaths at nadir,
a swath of 117 km (3 km overlap between the two swaths)
can be imaged. In this mode of operation, either panchromatic
or multispectral data can be collected, but not both
simultaneously.

SPOT has a number of benefits over other space-borne optical sensors. Its fine spatial
resolution and pointable sensors are the primary reasons for its popularity. The three band
multispectral data are well suited to displaying as false-colour images and the panchromatic
band can also be used to "sharpen" the spatial detail in the multispectral data.
SPOT allows applications requiring fine spatial
detail (such as urban mapping) to be addressed
while retaining the cost and timeliness
advantage of satellite data. The potential
applications of SPOT data are numerous.
Applications requiring frequent monitoring
(agriculture, forestry) are well served by the
SPOT sensors. The acquisition of stereoscopic
imagery from SPOT has played an important
role in mapping applications and in the
derivation of topographic information (Digital
Elevation Models - DEMs) from satellite data.

c) IRS
The Indian Remote Sensing (IRS) satellite series, combines features from both the Landsat
MSS/TM sensors and the SPOT HRV sensor. The third satellite in the series, IRS-1C, launched
in December, 1995 has three sensors: a single-channel panchromatic (PAN) high resolution
camera, a medium resolution four-channel Linear Imaging Self-scanning Sensor (LISS-III), and
a coarse resolution two-channel Wide Field Sensor (WiFS). The accompanying table outlines
the specific characteristics of each sensor.
In addition to its high spatial
resolution, the panchromatic sensor
can be steered up to 26° across-track,
enabling stereoscopic imaging and
increased revisit capabilities (as few
as five days), similar to SPOT. This
high resolution data is useful for
urban planning and mapping
applications.

2.13 Other Sensors
The three previous sections provide a representative overview of specific systems available for
remote sensing in the (predominantly) optical portions of the electromagnetic spectrum.
However, there are many other types of less common sensors which are used for remote
sensing purposes. We briefly touch on a few of these other types of sensors. The information is
not considered comprehensive but serves as an introduction to alternative imagery sources and
imaging concepts.
a) Video
Although coarser in spatial resolution than traditional photography or digital imaging, video
cameras provide a useful means of acquiring timely and inexpensive data and vocally
annotated imagery. Applications with these requirements include natural disaster management,
(fires, flooding), crop and disease assessment, environmental hazard control, and police
surveillance. Cameras used for video recording measure radiation in the visible, near infrared,
and sometimes mid-infrared portions of the EM spectrum. The image data are recorded onto
cassette, and can be viewed immediately.

b) FLIR
Forward Looking InfraRed (FLIR) systems operate in a similar manner to across-track thermal
imaging sensors, but provide an oblique rather than nadir perspective of the Earth´s surface.
Typically positioned on aircraft or helicopters, and imaging the area ahead of the platform,
FLIR systems provide relatively high spatial resolution imaging that can be used for military
applications, search and rescue operations, law enforcement, and forest fire monitoring.
c) Laser fluorosensor
Some targets fluoresce, or emit energy, upon receiving incident energy. This is not a simple
reflection of the incident radiation, but rather an absorption of the initial energy, excitation of
the molecular components of the target materials, and emission of longer wavelength
radiation which is then measured by the sensor. Laser fluorosensors illuminate the target with
a specific wavelength of radiation and are capable of detecting multiple wavelengths of
fluoresced radiation. This technology has been proven for ocean applications, such as
chlorophyll mapping, and pollutant detection, particularly for naturally occurring and accidental
oil slicks.

d) LiDAR
Lidar is an acronym for LIght Detection And Ranging, an active imaging technology very
similar to RADAR (see next paragraph). Pulses of laser light are emitted from the sensor and
energy reflected from a target is detected. The time required for the energy to reach the target
and return to the sensor determines the distance between the two. Lidar is used effectively for
measuring heights of features, such as forest canopy height relative to the ground surface,
and water depth relative to the water surface (laser profilometer). Lidar is also used in
atmospheric studies to examine the particle content of various layers of the Earth´s
atmosphere and acquire air density readings and monitor air currents.
e) RADAR
RADAR stands for RAdio Detection And Ranging. RADAR systems are active sensors which
provide their own source of electromagnetic energy. Active radar sensors, whether airborne or
spaceborne, emit microwave radiation in a series of pulses from an antenna, looking obliquely
at the surface perpendicular to the direction of motion. When the energy reaches the target,
some of the energy is reflected back towards the sensor. This backscattered microwave
radiation is detected, measured, and timed.

2.14 Data Reception, Transmission, and Processing
Data obtained during airborne remote sensing missions can be retrieved once the aircraft
lands. It can then be processed and delivered to the end user. However, data acquired from
satellite platforms need to be electronically transmitted to Earth, since the satellite continues
to stay in orbit during its operational lifetime. The technologies designed to accomplish this
can also be used by an aerial platform if the data are urgently needed on the surface.
There are three main options for transmitting data acquired
by satellites to the surface. The data can be directly
transmitted to Earth if a Ground Receiving Station (GRS) is
in the line of sight of the satellite (A). If this is not the case,
the data can be recorded on board the satellite (B) for
transmission to a GRS at a later time. Data can also be
relayed to the GRS through the Tracking and Data Relay
Satellite System (TDRSS) (C), which consists of a series of
communications satellites in geosynchronous orbit. The data
are transmitted from one satellite to another until they reach
the appropriate GRS.

The time required for the energy to travel to the target and return back to the sensor
determines the distance or range to the target. By recording the range and magnitude of the
energy reflected from all targets as the system passes by, a two-dimensional image of the
surface can be produced. Because RADAR provides its own energy source, images can be
acquired day or night. Also, microwave energy is able to penetrate through clouds and most
rain, making it an all-weather sensor.
The data are received at the GRS in a raw digital format. They may then, if required, be
processed to correct systematic, geometric and atmospheric distortions to the imagery, and
be translated into a standardized format. The data are written to some form of storage
medium such as tape, disk or CD. The data are typically archived at most receiving and
processing stations, and full libraries of data are managed by government agencies as well as
commercial companies responsible for each sensor's archives.
For many sensors it is possible to provide customers with quick-turnaround imagery
when they need data as quickly as possible after it is collected. Near real-time processing
systems are used to produce low resolution imagery in hard copy or soft copy (digital)
format within hours of data acquisition. Such imagery can then be faxed or transmitted
digitally to end users.

One application of this type of fast data processing is to provide imagery to ships sailing in the
Arctic, as it allows them to assess current ice conditions quickly in order to make navigation
decisions about the easiest/safest routes through the ice. Real-time processing of imagery in
airborne systems has been used, for example, to pass thermal infrared imagery to forest fire
fighters right at the scene.
Low resolution quick-look imagery is used to preview archived imagery prior to purchase. The
spatial and radiometric quality of these types of data products is degraded, but they are useful
for ensuring that the overall quality, coverage and cloud cover of the data is appropriate.

3.1 Introduction
In order to take advantage of and make good use of remote sensing data, we must be able to
extract meaningful information from the imagery. This brings us to the topic of discussion in
this chapter - interpretation and analysis - the sixth element of the remote sensing process
which we defined in Chapter 1. Interpretation and analysis of remote sensing imagery
involves the identification and/or measurement of various targets in an image in order to
extract useful information about them. Targets in remote sensing images may be any feature
or object which can be observed in an image, and have the following characteristics:
• Targets may be a point, line, or area feature. This
means that they can have any form, from a bus in a
parking lot or plane on a runway, to a bridge or
roadway, to a large expanse of water or a field.
• The target must be distinguishable; it must contrast
with other features around it in the image

Much interpretation and identification of targets in remote
sensing imagery is performed manually or visually, i.e. by a
human interpreter. In many cases this is done using imagery
displayed in a pictorial or photograph-type format,
independent of what type of sensor was used to collect the
data and how the data were collected. In this case we refer
to the data as being in analog format. As we discussed in
Chapter 1, remote sensing images can also be
represented in a computer as arrays of pixels, with each
pixel corresponding to a digital number, representing the
brightness level of that pixel in the image.
In this case, the data are in a digital format. Visual interpretation may also be performed by
examining digital imagery displayed on a computer screen. Both analogue and digital imagery
can be displayed as black and white (also called monochrome) images, or as colour images
(refer back to Chapter 1, Section 1.7) by combining different channels or bands representing
different wavelengths.

When remote sensing data are available in digital format, digital
processing and analysis may be performed using a computer.
Digital processing may be used to enhance data as a prelude to
visual interpretation. Digital processing and analysis may also be
carried out to automatically identify targets and extract information
completely without manual intervention by a human interpreter.
However, rarely is digital processing and analysis carried out as a
complete replacement for manual interpretation. Often, it is done to
supplement and assist the human analyst.
Manual interpretation and analysis dates back to the early beginnings of remote sensing for air
photo interpretation. Digital processing and analysis is more recent with the advent of
digital recording of remote sensing data and the development of computers. Both manual and
digital techniques for interpretation of remote sensing data have their respective advantages
and disadvantages. Generally, manual interpretation requires little, if any, specialized
equipment, while digital analysis requires specialized, and often expensive, equipment.

Manual interpretation is often limited to analyzing only a single channel of data or a single
image at a time due to the difficulty in performing visual interpretation with multiple images.
The computer environment is more amenable to handling complex images of several or many
channels or from several dates. In this sense, digital analysis is useful for simultaneous
analysis of many spectral bands and can process large data sets much faster than a human
interpreter. Manual interpretation is a subjective process, meaning that the results will vary
with different interpreters. Digital analysis is based on the manipulation of digital numbers in
a computer and is thus more objective, generally resulting in more consistent results.
However, determining the validity and accuracy of the results from digital processing can be
difficult.
It is important to reiterate that visual and digital analyses of remote sensing imagery are not
mutually exclusive. Both methods have their merits. In most cases, a mix of both methods
is usually employed when analyzing imagery. In fact, the ultimate decision of the utility and
relevance of the information extracted at the end of the analysis process, still must be made
by humans.

3.2 Elements of Visual Interpretation
As we noted in the previous section, analysis of remote sensing imagery involves the
identification of various targets in an image, and those targets may be environmental or
artificial features which consist of points, lines, or areas. Targets may be defined in terms of
the way they reflect or emit radiation. This radiation is measured and recorded by a sensor,
and ultimately is depicted as an image product such as an air photo or a satellite image.
What makes interpretation of imagery more difficult than the everyday visual interpretation
of our surroundings? For one, we lose our sense of depth when viewing a two-dimensional
image, unless we can view it stereoscopically so as to simulate the third dimension of
height. Indeed, interpretation benefits greatly in many applications when images are viewed
in stereo, as visualization (and therefore, recognition) of targets is enhanced dramatically.
Viewing objects from directly above also provides a very different perspective than what we
are familiar with. Combining an unfamiliar perspective with a very different scale and lack
of recognizable detail can make even the most familiar object unrecognizable in an image.
Finally, we are used to seeing only the visible wavelengths, and the imaging of wavelengths
outside of this window is more difficult for us to comprehend.

Recognizing targets is the key to interpretation and information extraction. Observing the
differences between targets and their backgrounds involves comparing different targets based
on any, or all, of the visual elements of tone, shape, size, pattern, texture, shadow, and
association. Visual interpretation using these elements is often a part of our daily lives,
whether we are conscious of it or not.
Examining satellite images on the weather report, or
following high speed chases by views from a helicopter are
all familiar examples of visual image interpretation.
Identifying targets in remotely sensed images based on
these visual elements allows us to further interpret and
analyze. The nature of each of these interpretation elements
is described below, along with an image example of each.
tone also allows the elements of shape, texture, and pattern
of objects to be distinguished.

a) Shape refers to the general form, structure, or outline of
individual objects. Shape can be a very distinctive clue for
interpretation. Straight edge shapes typically represent urban or
agricultural (field) targets, while natural features, such as forest
edges, are generally more irregular in shape, except where man has
created a road or clear cuts.
A quick approximation of target size can direct interpretation to
an appropriate result more quickly. For example, if an
interpreter had to distinguish zones of land use, and had
identified an area with a number of buildings in it, large
buildings such as factories or warehouses would suggest
commercial property, whereas small buildings would indicate
residential use.
b) Size of objects in an image is a function of scale. It is important
to assess the size of a target relative to other objects in a scene, as
well as the absolute size, to aid in the interpretation of that target.

c) Pattern refers to the spatial arrangement of visibly discernible objects. Typically an
orderly repetition of similar tones and textures will produce a distinctive and ultimately
recognizable pattern. Orchards with evenly spaced trees, and urban streets with regularly
spaced houses are good examples of pattern.
d) Texture refers to the arrangement and frequency of tonal
variation in particular areas of an image. Rough textures would
consist of a mottled tone where the grey levels change abruptly
in a small area, whereas smooth textures would have very little
tonal variation. Smooth textures are most often the result of
uniform, even surfaces, such as fields, asphalt, or grasslands. A
target with a rough surface and irregular structure, such as a
forest canopy, results in a rough textured appearance. Texture is
one of the most important elements for distinguishing features in
radar imagery

e) Shadow is also helpful in interpretation as it may provide an
idea of the profile and relative height of a target or targets which
may make identification easier. However, shadows can also reduce
or eliminate interpretation in their area of influence, since targets
within shadows are much less (or not at all) discernible from their
surroundings. Shadow is also useful for enhancing or identifying
topography and landforms, particularly in radar imagery.
f) Association takes into account the relationship between other
recognizable objects or features in proximity to the target of interest.
The identification of features that one would expect to associate with
other features may provide information to facilitate identification. In the
example given above, commercial properties may be associated with
proximity to major transportation routes, whereas residential areas
would be associated with schools, playgrounds, and sports fields.

In our example, a lake is associated with boats, a marina, and adjacent recreational land.
3.3 Digital Image Processing
In today's world of advanced technology where most remote
sensing data are recorded in digital format, virtually all image
interpretation and analysis involves some element of digital
processing. Digital image processing may involve numerous
procedures including formatting and correcting of the data, digital
enhancement to facilitate better visual interpretation, or even
automated classification of targets and features entirely by
computer.
In order to process remote sensing imagery digitally, the data must be recorded and available
in a digital form suitable for storage on a computer tape or disk. Obviously, the other
requirement for digital image processing is a computer system, sometimes referred to as an
image analysis system, with the appropriate hardware and software to process the data.
Several commercially available software systems have been developed specifically for remote
sensing image processing and analysis.

For discussion purposes, most of the common image processing functions available in image
analysis systems can be categorized into the following four categories:
Preprocessing
Image Enhancement
Image Transformation
Image Classification and Analysis
Preprocessing functions involve those operations that are normally required prior to the main
data analysis and extraction of information, and are generally grouped as radiometric or
geometric corrections. Radiometric corrections include correcting the data for sensor
irregularities and unwanted sensor or atmospheric noise, and converting the data so they
accurately represent the reflected or emitted radiation measured by the sensor. Geometric
corrections include correcting for geometric distortions due to sensor-Earth geometry
variations, and conversion of the data to real world coordinates (e.g. latitude and longitude)
on the Earth's surface.

The objective of the second group of image processing functions grouped under the term of image
enhancement, is solely to improve the appearance of the imagery to assist in visual interpretation and
analysis. Examples of enhancement functions include contrast stretching to increase the tonal distinction
between various features in a scene, and spatial filtering to enhance (or suppress) specific spatial patterns in
an image.
Image transformations are operations similar in concept to those for image enhancement.
However, unlike image enhancement operations which are normally applied only to a single
channel of data at a time, image transformations usually involve combined processing of data
from multiple spectral bands. Arithmetic operations (i.e. subtraction, addition, multiplication,
division) are performed to combine and transform the original bands into "new" images
which better display or highlight certain features in the scene.

We will look at some of these operations including various methods of spectral or band
ratioing, and a procedure called principal components analysis which is used to more
efficiently represent the information in multichannel imagery.
Image classification and analysis operations are used to digitally identify and classify pixels
in the data. Classification is usually performed on multi-channel data sets (A) and this
process assigns each pixel in an image to a particular class or theme (B) based on statistical
characteristics of the pixel brightness values. There are a variety of approaches taken to
perform digital classification. We will briefly describe the two generic approaches which are
used most often, namely supervised and unsupervised classification.

3.4 Pre-processing
Pre-processing operations, sometimes referred to as image restoration and rectification, are
intended to correct for sensor- and platform-specific radiometric and geometric distortions of
data. Radiometric corrections may be necessary due to variations in scene illumination and
viewing geometry, atmospheric conditions, and sensor noise and response. Each of these will
vary depending on the specific sensor and platform used to acquire the data and the
conditions during data acquisition. Also, it may be desirable to convert and/or calibrate the
data to known (absolute) radiation or reflectance units to facilitate comparison between data.
Variations in illumination and viewing geometry
between images (for optical sensors) can be corrected by
modeling the geometric relationship and distance
between the area of the Earth's surface imaged, the sun,
and the sensor. This is often required so as to be able to
more readily compare images collected by different
sensors at different dates or times, or to mosaic multiple
images from a single sensor while maintaining uniform
illumination conditions from scene to scene.

As we learned in Chapter 1, scattering of radiation occurs as it passes through and interacts
with the atmosphere. This scattering may reduce, or attenuate, some of the energy
illuminating the surface. In addition, the atmosphere will further attenuate the signal
propagating from the target to the sensor. Various methods of atmospheric correction can be
applied ranging from detailed modeling of the atmospheric conditions during data acquisition,
to simple calculations based solely on the image data. An example of the latter method is to
examine the observed brightness values (digital numbers), in an area of shadow or for a
very dark object (such as a large clear lake - A) and determine the minimum value (B). The
correction is applied by subtracting the minimum observed value, determined for each specific
band, from all pixel values in each respective band.

Since scattering is wavelength dependent (Chapter 1), the minimum values will vary from band
to band. This method is based on the assumption that the reflectance from these features, if the
atmosphere is clear, should be very small, if not zero. If we observe values much greater than
zero, then they are considered to have resulted from atmospheric scattering.
Noise in an image may be due to irregularities or errors that
occur in the sensor response and/or data recording and
transmission. Common forms of noise include systematic
striping or banding and dropped lines. Both of these
effects should be corrected before further enhancement or
classification is performed. Striping was common in early
Landsat MSS data due to variations and drift in the
response over time of the six MSS detectors. The "drift"
was different for each of the six detectors, causing the same
brightness to be represented differently by each detector.

The overall appearance was thus a 'striped‘ effect. The corrective process made a relative
correction among the six sensors to bring their apparent values in line with each other. Dropped
lines occur when there are systems errors which result in missing or defective data along a scan
line. Dropped lines are normally 'corrected‘ by replacing the line with the pixel values in the
line above or below, or with the average of the two.
For many quantitative applications of remote sensing data, it
is necessary to convert the digital numbers to measurements
in units which represent the actual reflectance or emittance
from the surface. This is done based on detailed knowledge
of the sensor response and the way in which the analog signal
(i.e. the reflected or emitted radiation) is converted to a
digital number, called analog-to-digital (A-to-D) conversion.
By solving this relationship in the reverse direction, the
absolute radiance can be calculated for each pixel, so that
comparisons can be accurately made over time and between
different sensors.

These distortions may be due to several factors, including: the perspective of the sensor optics;
the motion of the scanning system; the motion of the platform; the platform altitude, attitude,
and velocity; the terrain relief; and, the curvature and rotation of the Earth. Geometric
corrections are intended to compensate for these distortions so that the geometric
representation of the imagery will be as close as possible to the real world. Many of these
variations are systematic, or predictable in nature and can be accounted for by accurate
modeling of the sensor and platform motion and the geometric relationship of the platform
with the Earth. Other unsystematic, or random, errors cannot be modeled and corrected in
this way. Therefore, geometric registration of the imagery to a known ground coordinate
system must be performed.
The geometric registration process involves
identifying the image coordinates (i.e. row, column) of
several clearly discernible points, called ground control
points (or GCPs), in the distorted image (A - A1 to A4),
and matching them to their true positions in ground
coordinates (e.g. latitude, longitude).

The true ground coordinates are typically measured from a map (B - B1 to B4), either in paper
or digital format. This is image-to-map registration. Once several well-distributed GCP
pairs have been identified, the coordinate information is processed by the computer to
determine the proper transformation equations to apply to the original (row and column)
image coordinates to map them into their new ground coordinates. Geometric registration
may also be performed by registering one (or more) images to another image, instead of to
geographic coordinates. This is called image-to-image registration and is often done prior to
performing various image transformation procedures, which will be discussed in section 4.6,
or for multitemporal image comparison.
In order to actually geometrically correct the original distorted
image, a procedure called resampling is used to determine the
digital values to place in the new pixel locations of the
corrected output image. The resampling process calculates the
new pixel values from the original digital pixel values in the
uncorrected image.

There are three common methods for resampling: nearest neighbour, bilinear interpolation,
and cubic convolution. Nearest neighbour resampling uses the digital value from the pixel in
the original image which is nearest to the new pixel location in the corrected image. This is the
simplest method and does not alter the original values, but may result in some pixel values
being duplicated while others are lost. This method also tends to result in a disjointed or
blocky image appearance.
Bilinear interpolation resampling takes a weighted average of
four pixels in the original image nearest to the new pixel location.
The averaging process alters the original pixel values and creates
entirely new digital values in the output image. This may be
undesirable if further processing and analysis, such as
classification based on spectral response, is to be done. If this is
the case, resampling may best be done after the classification
process.

Cubic convolution resampling goes even further to calculate a distance weighted average of a
block of sixteen pixels from the original image which surround the new output pixel location.
As with bilinear interpolation, this method results in completely new pixel values. However,
these two methods both produce images which have a much sharper appearance and avoid the
blocky appearance of the nearest neighbour method.
3.5 Image Enhancement
Enhancements are used to make it easier for visual interpretation and understanding of
imagery. The advantage of digital imagery is that it allows us to manipulate the digital pixel
values in an image. Although radiometric corrections for illumination, atmospheric influences,
and sensor characteristics may be done prior to distribution of data to the user, the image may
still not be optimized for visual interpretation. Remote sensing devices, particularly those
operated from satellite platforms, must be designed to cope with levels of target/background
energy which are typical of all conditions likely to be encountered in routine use. With large
variations in spectral response from a diverse range of targets (e.g. forest, deserts,
snowfields, water, etc.) no generic radiometric correction could optimally account for and
display the optimum brightness range and contrast for all targets.

Thus, for each application and each image, a custom adjustment of the range and distribution
of brightness values is usually necessary.
In raw imagery, the useful data often populates only a small
portion of the available range of digital values (commonly 8 bits
or 256 levels). Contrast enhancement involves changing the
original values so that more of the available range is used,
thereby increasing the
contrast between targets and their backgrounds. The key to
understanding contrast enhancements is to understand the
concept of an image histogram. A histogram is a graphical
representation of the brightness values that comprise an image.
The brightness values (i.e. 0-255) are displayed along the x-axis
of the graph. The frequency of occurrence of each of these
values in the image is shown on the y-axis.

By manipulating the range of digital values in an
image, graphically represented by its histogram, we
can apply various enhancements to the data. There are
many different techniques and methods of enhancing
contrast and detail in an image; we will cover only a
few common ones here. The simplest type of
enhancement is a linear contrast stretch.
This involves identifying lower and upper bounds from the histogram (usually the minimum
and maximum brightness values in the image) and applying a transformation to stretch this
range to fill the full range. In our example, the minimum value (occupied by actual data) in
the histogram is 84 and the maximum value is 153. These 70 levels occupy less than one-
third of the full 256 levels available. A linear stretch uniformly expands this small range to
cover the full range of values from 0 to 255. This enhances the contrast in the image with
light toned areas appearing lighter and dark areas appearing darker, making visual
interpretation much easier.

This graphic illustrates the increase in contrast in an image before (left) and after
(right) a linear contrast stretch.
A uniform distribution of the input range
of values across the full range may not
always be an appropriate enhancement,
particularly if the input range is not
uniformly distributed.
In this case, a histogram-equalizedstretch may be better.
This stretch assignsmore display values (range) to the
frequently occurring portions of the histogram. In this way,
the detail in these areas will be better enhanced relative to
those areas of the original histogram where values occur
less frequently. In other cases, it may be desirable to
enhance the contrast in only a specific portion of the
histogram.

For example, suppose we have an image of the mouth of a river, and the water portions of the
image occupy the digital values from 40 to 76 out of the entire image histogram. If we wished
to enhance the detail in the water, perhaps to see variations in sediment load, we could
stretch only that small portion of the histogram represented by the water (40 to 76) to the full
grey level range (0 to 255). All pixels below or above these values would be assigned to 0 and
255, respectively, and the detail in these areas would be lost. However, the detail in the water
would be greatly enhanced.
For example, suppose we have an image of the
mouth of a river, and the water portions of the
image occupy the digital values from 40 to 76
out of the entire image histogram. If we wished
to enhance the detail in the water, perhaps to see
variations in sediment load, we could
stretch only that small portion of the histogram
represented by the water (40 to 76) to the full
grey level range (0 to 255).

All pixels below or above these values would be assigned to 0 and
255, respectively, and the detail in these areas would be lost. However, the detail in the
water would be greatly enhanced.
Spatial filters are designed to highlight or suppress specific features in an image based
on their spatial frequency. Spatial frequency is related to the concept of image texture,
which we discussed in section 4.2. It refers to the frequency of the variations in tone that
appear in an image. "Rough" textured areas of an image, where the changes in tone are
abrupt over a small area, have high spatial frequencies, while "smooth" areas with little
variation in tone over several pixels, have low spatial frequencies.
A common filtering procedure involves
moving a 'window' of a few pixels in
dimension (e.g. 3x3, 5x5, etc.) over each
pixel in the image, applying a
mathematical calculation using the pixel
values under that window, and replacing
the central pixel with the new value.

The window is moved along in both the row and column dimensions one pixel at a time and
the calculation is repeated until the entire image has been filtered and a "new" image has been
generated. By varying the calculation performed and the weightings of the individual pixels in
the filter window, filters can be designed to enhance or suppress different types of features.
A low-pass filter is designed to emphasize larger, homogeneous areas of similar tone and
reduce the smaller detail in an image. Thus, low-pass filters generally serve to smooth the
appearance of an image. Average and median filters, often used for radar imagery (and
described in Chapter 3), are examples of low-pass filters.
High-pass filters do the opposite and serve to sharpen the
appearance of fine detail in an image. One
implementation of a high-pass filter first applies a low-
pass filter to an image and then subtracts the result from
the original, leaving behind only the high spatial
frequency information.

Directional, or edge detection filters are designed to highlight linear features, such as roads or
field boundaries. These filters can also be designed to enhance features which are oriented in
specific directions. These filters are useful in applications such as geology, for the detection of
linear geologic structures.
3.6 Image Transformations
Image transformations typically involve the manipulation of
multiple bands of data, whether from a single multispectral image
or from two or more images of the same area acquired at different
times (i.e. multitemporal image data). Either way, image
transformations generate "new" images from two or more sources
which highlight particular features or properties of interest, better
than the original input images.

Basic image transformations apply simple arithmetic operations to the image data. Image
subtraction is often used to identify changes that have occurred between images collected on
different dates. Typically, two images which have been geometrically registered (see section
4.4), are used with the pixel (brightness) values in one image (1) being subtracted from the
pixel values in the other (2). Scaling the resultant image (3) by adding a constant (127 in this
case) to the output values will result in a suitable 'difference' image. In such an image, areas
where there has been little or no change (A) between the original images, will have resultant
brightness values around 127 (mid-grey tones), while those areas where significant change has
occurred (B) will have values higher or lower than 127 - brighter or darker depending on the
'direction' of change in reflectance between the two images . This type of image transform can
be useful for mapping changes in urban development around cities and for identifying areas
where deforestation is occurring, as in this example.
Image division or spectral ratioing is one of the most common transforms applied to image
data. Image ratioing serves to highlight subtle variations in the spectral responses of various
surface covers.

By ratioing the data from two different spectral bands, the
resultant image enhances variations in the slopes of the
spectral reflectance curves between the two different spectral
ranges that may otherwise be masked by the pixel brightness
variations in each of the bands. The following example
illustrates the concept of spectral ratioing. Healthy vegetation
reflects strongly in the near-infrared portion of the spectrum
while absorbing strongly in the visible red. Other surface
types, such as soil and water, show near equal reflectances in
both the near-infrared and red portions.
Thus, a ratio image of Landsat MSS Band 7 (Near-Infrared - 0.8 to 1.1 mm) divided by Band
5 (Red - 0.6 to 0.7 mm) would result in ratios much greater than 1.0 for vegetation, and
ratios around 1.0 for soil and water. Thus the discrimination of vegetation from other surface
cover types is significantly enhanced. Also, we may be better able to identify areas of
unhealthy or stressed vegetation, which show low near-infrared reflectance, as the ratios
would be lower than for healthy green vegetation.

Another benefit of spectral ratioing is that, because we are looking at relative values (i.e.
ratios) instead of absolute brightness values, variations in scene illumination as a result of
topographic effects are reduced. Thus, although the absolute reflectances for forest covered
slopes may vary depending on their orientation relative to the sun's illumination, the ratio of
their reflectances between the two bands should always be very similar. More complex ratios
involving the sums of and differences between spectral bands for various sensors, have been
developed for monitoring vegetation conditions. One widely used image transform is the
Normalized Difference Vegetation Index (NDVI) which has been used to monitor vegetation
conditions on continental and global scales using the Advanced Very High Resolution
Radiometer (AVHRR) sensor onboard the NOAA series of satellites.
Different bands of multispectral data are often highly correlated and thus contain similar
information. For example, Landsat MSS Bands 4 and 5 (green and red, respectively) typically
have similar visual appearances since reflectances for the same surface cover types are almost
equal. Image transformation techniques based on complex processing of the statistical
characteristics of multi-band data sets can be used to reduce this data redundancy and
correlation between bands.

One such transform is called principal components
analysis. The objective of this transformation is to
reduce the dimensionality (i.e. the number of bands) in
the data, and compress as much of the information in
the original bands into fewer bands. The "new" bands
that result from this statistical procedure are called
components.
This process attempts to maximize (statistically) the amount of information (or variance) from
the original data into the least number of new components. As an example of the use of
principal components analysis, a seven band Thematic Mapper (TM) data set may be
transformed such that the first three principal components contain over 90 percent of the
information in the original seven bands. Interpretation and analysis of these three bands of data,
combining them either visually or digitally, is simpler and more efficient than trying to use all
of the original seven bands. Principal components analysis, and other complex transforms, can
be used either as an enhancement technique to improve visual interpretation or to reduce the
number of bands to be used as input to digital classification procedures

3.7 Image Classification and Analysis
A human analyst attempting to classify features in an image uses the elements of visual
interpretation to identify homogeneous groups of pixels which represent various features or
land cover classes of interest. Digital image classification uses the spectral information
represented by the digital numbers in one or more spectral bands, and attempts to classify
each individual pixel based on this spectral information.
This type of classification is termed
spectral pattern recognition. In either
case, the objective is to assign all pixels in
the image to particular classes or themes
(e.g. water, coniferous forest, deciduous
forest, corn, wheat, etc.). The resulting
classified image is comprised of a mosaic
of pixels, each of which belong to a
particular theme, and is essentially a
thematic "map" of the original image.

Common classification procedures can be broken down into two broad subdivisions based on
the method used: supervised classification and unsupervised classification.
In a supervised classification, the analyst identifies in the
imagery homogeneous representative samples of the
different surface cover types (information classes) of
interest. These samples are referred to as training areas.
The selection of appropriate training areas is based on the
analyst's familiarity with the geographical area and their
knowledge of the actual surface cover types present in the
image.
Thus, the analyst is "supervising" the categorization of a set of specific classes. The
numerical information in all spectral bands for the pixels comprising these areas are used to
"train" the computer to recognize spectrally similar areas for each class. The computer uses a
special program or algorithm (of which there are several variations), to determine the
numerical "signatures" for each training class.

Once the computer has determined the signatures for each class, each pixel in the image is
compared to these signatures and labeled as the class it most closely "resembles" digitally.
Thus, in a supervised classification we are first identifying the information classes which are
then used to determine the spectral classes which represent them.
Unsupervised classification in essence reverses the supervised classification process.
Spectral classes are grouped first, based solely on the numerical information in the data,
and are then matched by the analyst to information classes (if possible). Programs, called
clustering algorithms, are used to determine the natural (statistical) groupings or
structures in the data.
Usually, the analyst specifies how many groups or clusters are
to be looked for in the data. In addition to specifying the
desired number of classes, the analyst may also specify
parameters related to the separation distance among the
clusters and the variation within each cluster.

The final result of this iterative clustering process may result in some clusters that the analyst
will want to subsequently combine, or clusters that should be broken down further - each of
these requiring a further application of the clustering algorithm. Thus, unsupervised
classification is not completely without human intervention. However, it does not start with a
pre-determined set of classes as in a supervised classification.
3.8 Data Integration and Analysis
In the early days of analog remote sensing when the only remote sensing data source was
aerial photography, the capability for integration of data from different sources was limited.
Today, with most data available in digital format from a wide array of sensors, data integration
is a common method used for interpretation and analysis. Data integration fundamentally
involves the combining or merging of data from multiple sources in an effort to extract better
and/or more information. This may include data that are multitemporal, multiresolution,
multisensor, or multi-data type in nature.
Imagery collected at different times is integrated to identify areas of change. Multitemporal
change detection can be achieved through simple methods such as these, or by other more
complex approaches such as multiple classification comparisons or classifications using
integrated multitemporal data sets.

Multiresolution data merging is useful for a variety of
applications. The merging of data of a higher spatial
resolution with data of lower resolution (resolution merge)
can significantly sharpen the spatial detail in an image and
enhance the discrimination of features. Landsat data are
well suited to this approach as the 15 meters panchromatic
data can be easily merged with the 30 meters multispectral
data (Pansharpenging). Additionally, the multispectral data
serve to retain good spectral resolution while the
panchromatic data provide the improved spatial resolution.
Data from different sensors may also be merged, bringing in the concept of multisensor data
fusion. An excellent example of this technique is the combination of multispectral optical
data with radar imagery. These two diverse spectral representations of the surface can
provide complementary information. The optical data provide detailed spectral information
useful for discriminating between surface cover types, while the radar imagery highlights the
structural detail in the image.

Applications of multisensor data integration generally
require that the data be geometrically registered, either to
each other or to a common geographic coordinate system or
map base. This also allows other ancillary (supplementary)
data sources to be integrated with the remote sensing data.
For example, elevation data in digital form, called Digital
Elevation or Digital Terrain Models (DEMs/DTMs), may
be combined with remote sensing data for a variety of
purposes.
DEMs/DTMs may be useful in image classification, as effects due to terrain and slope
variability can be corrected, potentially increasing the accuracy of the resultant classification.
DEMs/DTMs are also useful for generating three-dimensional perspective views by draping
remote sensing imagery over the elevation data, enhancing visualization of the area imaged.

Introduction to Remote Sensing- Remote sensing” is the science (and to some extent, art) of acquiring information about the Earth's surface without actually being in contact with it.

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Introduction to Remote Sensing- Remote sensing” is the science (and to some extent, art) of acquiring information about the Earth's surface without actually being in contact with it.