{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T05:39:04Z","timestamp":1780983544596,"version":"3.54.1"},"reference-count":62,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,3,3]],"date-time":"2024-03-03T00:00:00Z","timestamp":1709424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CAST Innovation foundation Program"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Hyperspectral anomaly detection is used to recognize unusual patterns or anomalies in hyperspectral data. Currently, many spectral\u2013spatial detection methods have been proposed with a cascaded manner; however, they often neglect the complementary characteristics between the spectral and spatial dimensions, which easily leads to yield high false alarm rate. To alleviate this issue, a spectral\u2013spatial information fusion (SSIF) method is designed for hyperspectral anomaly detection. First, an isolation forest is exploited to obtain spectral anomaly map, in which the object-level feature is constructed with an entropy rate segmentation algorithm. Then, a local spatial saliency detection scheme is proposed to produce the spatial anomaly result. Finally, the spectral and spatial anomaly scores are integrated together followed by a domain transform recursive filtering to generate the final detection result. Experiments on five hyperspectral datasets covering ocean and airport scenes prove that the proposed SSIF produces superior detection results over other state-of-the-art detection techniques.<\/jats:p>","DOI":"10.3390\/s24051652","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T04:36:21Z","timestamp":1709526981000},"page":"1652","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Spectral\u2013Spatial Feature Fusion for Hyperspectral Anomaly Detection"],"prefix":"10.3390","volume":"24","author":[{"given":"Shaocong","family":"Liu","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing Satellite, China Academy of Space Technology (CAST), Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing Satellite, China Academy of Space Technology (CAST), Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing Satellite, China Academy of Space Technology (CAST), Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-2428-3354","authenticated-orcid":false,"given":"Xianfei","family":"Qiu","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing Satellite, China Academy of Space Technology (CAST), Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tinghao","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing Satellite, China Academy of Space Technology (CAST), Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Cao","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing Satellite, China Academy of Space Technology (CAST), Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-1690-4886","authenticated-orcid":false,"given":"Donghui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing Satellite, China Academy of Space Technology (CAST), Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, W., Guo, H., Liu, S., and Wu, S. 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