{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T15:05:33Z","timestamp":1783695933719,"version":"3.55.0"},"reference-count":39,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T00:00:00Z","timestamp":1592179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Basic Science Research Programs of the Ministry of Education","award":["Grant NRF-2018R1A2B6005105"],"award-info":[{"award-number":["Grant NRF-2018R1A2B6005105"]}]},{"name":"National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT)","award":["No. 2019R1A5A8080290"],"award-info":[{"award-number":["No. 2019R1A5A8080290"]}]},{"name":"Ministry of Science, ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) support program supervised by the IITP(Institute for Information &amp; communications Technology Planning &amp; Evaluation)","award":["IITP-2020-2016-0-00313"],"award-info":[{"award-number":["IITP-2020-2016-0-00313"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, a modified encoder-decoder structured fully convolutional network (ED-FCN) is proposed to generate the camera-like color image from the light detection and ranging (LiDAR) reflection image. Previously, we showed the possibility to generate a color image from a heterogeneous source using the asymmetric ED-FCN. In addition, modified ED-FCNs, i.e., UNET and selected connection UNET (SC-UNET), have been successfully applied to the biomedical image segmentation and concealed-object detection for military purposes, respectively. In this paper, we apply the SC-UNET to generate a color image from a heterogeneous image. Various connections between encoder and decoder are analyzed. The LiDAR reflection image has only 5.28% valid values, i.e., its data are extremely sparse. The severe sparseness of the reflection image limits the generation performance when the UNET is applied directly to this heterogeneous image generation. In this paper, we present a methodology of network connection in SC-UNET that considers the sparseness of each level in the encoder network and the similarity between the same levels of encoder and decoder networks. The simulation results show that the proposed SC-UNET with the connection between encoder and decoder at two lowest levels yields improvements of 3.87 dB and 0.17 in peak signal-to-noise ratio and structural similarity, respectively, over the conventional asymmetric ED-FCN. The methodology presented in this paper would be a powerful tool for generating data from heterogeneous sources.<\/jats:p>","DOI":"10.3390\/s20123387","type":"journal-article","created":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T12:16:57Z","timestamp":1592223417000},"page":"3387","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Color Image Generation from LiDAR Reflection Data by Using Selected Connection UNET"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-8464-9362","authenticated-orcid":false,"given":"Hyun-Koo","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38544, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-6049-1759","authenticated-orcid":false,"given":"Kook-Yeol","family":"Yoo","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38544, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-1719-7853","authenticated-orcid":false,"given":"Ho-Youl","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38544, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Reymann, C., and Lacroix, S. (October, January 28). Improving LiDAR point cloud classification using intensities and multiple echoes. Proceedings of the 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.","DOI":"10.1109\/IROS.2015.7354098"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4224","DOI":"10.1109\/TII.2018.2822828","article-title":"Object Classification Using CNN-Based Fusion of Vision and LIDAR in Autonomous Vehicle Environment","volume":"14","author":"Gao","year":"2018","journal-title":"IEEE Trans. Ind. Informat."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yu, L., Li, X., Fu, C.W., Cohen-Or, D., and Heng, P.A. (2018, January 18\u201323). PU-Net: Point Cloud Upsampling Network. Proceedings of the Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00295"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wurm, K.M., K\u00fcmmerle, R., Stachniss, C., and Burgard, W. (2009, January 11\u201315). Improving robot navigation in structured outdoor environments by identifying vegetation from laser data. Proceedings of the 2009 IEEE\/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO, USA.","DOI":"10.1109\/IROS.2009.5354530"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"085203","DOI":"10.1088\/1361-6501\/aa76a3","article-title":"Automatic extraction of pavement markings on streets from point cloud data of mobile LiDAR","volume":"28","author":"Gao","year":"2017","journal-title":"Meas. Sci. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"McManus, C., Furgale, P., and Barfoot, T.D. (2011, January 9\u201313). Towards appearance-based methods for lidar sensors. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980098"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tatoglu, A., and Pochiraju, K. (2012, January 14\u201318). Point cloud segmentation with LIDAR reflection intensity behavior. Proceedings of the 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA.","DOI":"10.1109\/ICRA.2012.6225224"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dewan, A., Oliveira, G.L., and Burgard, W. (2017, January 24\u201328). Deep semantic classification for 3D LiDAR data. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8206198"},{"key":"ref_9","unstructured":"Radi, H., and Ali, W. (2019). VolMap: A Real-time Model for Semantic Segmentation of a LiDAR surrounding view. arXiv."},{"key":"ref_10","first-page":"1","article-title":"Deep Learning Based Gray Image Generation from 3D LiDAR Reflection Intensity","volume":"14","author":"Kim","year":"2019","journal-title":"IEMEK J. Embed. Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Milz, S., Simon, M., Fischer, K., and P\u00f6pperl, M. (2019). Points2Pix: 3D Point-Cloud to Image Translation using conditional Generative Adversarial Networks. arXiv.","DOI":"10.1007\/978-3-030-33676-9_27"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kim, H.K., Yoo, K.Y., Park, J.H., and Jung, H.Y. (2019). Asymmetric Encoder-Decoder Structured FCN Based LiDAR to Color Image Generation. Sensors, 19.","DOI":"10.3390\/s19214818"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2576","DOI":"10.1109\/LRA.2019.2904733","article-title":"Rtfnet: Rgb-thermal fusion network for semantic segmentation of urban scenes","volume":"4","author":"Sun","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1007\/s11128-014-0841-8","article-title":"Quantum image scaling using nearest neighbor interpolation","volume":"14","author":"Jiang","year":"2015","journal-title":"Quantum Inf. Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1007\/s00477-008-0226-6","article-title":"Statistical approach to inverse distance interpolation","volume":"23","author":"Babak","year":"2009","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., and Han, B. (2015, January 7\u201313). Learning deconvolution network for semantic segmentation. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kim, H.K., Yoo, K.Y., Park, J.H., and Jung, H.Y. (2019). Traffic light recognition based on binary semantic segmentation network. Sensors, 19.","DOI":"10.3390\/s19071700"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1016\/j.ijleo.2019.04.034","article-title":"Concealed object segmentation in terahertz imaging via adversarial learning","volume":"185","author":"Liang","year":"2019","journal-title":"Optik"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.knosys.2013.11.006","article-title":"A new user similarity model to improve the accuracy of collaborative filtering","volume":"56","author":"Liu","year":"2014","journal-title":"Knowl. Based Syst."},{"key":"ref_25","unstructured":"Huang, Z., and Wang, N. (2017). Like What You Like: Knowledge Distill via Neuron Selectivity Transfer. arXiv."},{"key":"ref_26","unstructured":"Clevert, D.A., Unterthiner, T., and Hochreiter, S. (2015). Fast and accurate deep network learning by exponential linear units (elus). arXiv."},{"key":"ref_27","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., Krishnan, D., Taylor, G.W., and Fergus, R. (2010, January 13\u201318). Deconvolutional networks. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539957"},{"key":"ref_29","first-page":"111","article-title":"Performance analysis of various activation functions in generalized MLP architectures of neural networks","volume":"1","author":"Karlik","year":"2011","journal-title":"Int. J. Intell. Syst."},{"key":"ref_30","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/S0893-6080(98)00010-0","article-title":"Automatic early stopping using cross validation: Quantifying the criteria","volume":"11","author":"Prechelt","year":"1998","journal-title":"Neural Netw."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The KITTI dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1109\/TPAMI.2009.187","article-title":"Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation","volume":"32","author":"Rodriguez","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Murty, M.N., and Devi, V.S. (2011). Pattern Recognition: An Algorithmic Approach, Springer.","DOI":"10.1007\/978-0-85729-495-1"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hore, A., and Ziou, D. (2010, January 23\u201326). Image Quality Metrics: PSNR vs. SSIM. Proceedings of the 2010 20th International Conference on Pattern Recognition (ICPR), Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.579"},{"key":"ref_36","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). Tensorflow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, Savannah, GA, USA."},{"key":"ref_37","unstructured":"(2019, October 08). Keras. Available online: https:\/\/2.zoppoz.workers.dev:443\/https\/keras.io."},{"key":"ref_38","unstructured":"LeCun, Y., and Bengio, Y. (1995). Convolutional networks for images, speech, and time series. The Handbook of Brain Theory and Neural Networks, MIT Press."},{"key":"ref_39","unstructured":"Dumoulin, V., and Visin, F. (2016). A guide to convolution arithmetic for deep learning. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/20\/12\/3387\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:39:18Z","timestamp":1760175558000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/20\/12\/3387"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,15]]},"references-count":39,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["s20123387"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/s20123387","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,15]]}}}