{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T17:28:05Z","timestamp":1769016485618,"version":"3.49.0"},"reference-count":37,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,10,2]],"date-time":"2021-10-02T00:00:00Z","timestamp":1633132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62005049"],"award-info":[{"award-number":["62005049"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2020J01451"],"award-info":[{"award-number":["2020J01451"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Education and Scientific Research Foundation for Young Teachers in Fujian Province","award":["JAT190003"],"award-info":[{"award-number":["JAT190003"]}]},{"name":"Fuzhou University Research Project","award":["GXRC-19052"],"award-info":[{"award-number":["GXRC-19052"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The spectral information contained in the hyperspectral images (HSI) distinguishes the intrinsic properties of a target from the background, which is widely used in remote sensing. However, the low imaging speed and high data redundancy caused by the high spectral resolution of imaging spectrometers limit their application in scenarios with the real-time requirement. In this work, we achieve the precise detection of camouflaged targets based on snapshot multispectral imaging technology and band selection methods in urban-related scenes. Specifically, the camouflaged target detection algorithm combines the constrained energy minimization (CEM) algorithm and the improved maximum between-class variance (OTSU) algorithm (t-OTSU), which is proposed to obtain the initial target detection results and adaptively segment the target region. Moreover, an object region extraction (ORE) algorithm is proposed to obtain a complete target contour that improves the target detection capability of multispectral images (MSI). The experimental results show that the proposed algorithm has the ability to detect different camouflaged targets by using only four bands. The detection accuracy is above 99%, and the false alarm rate is below 0.2%. The research achieves the effective detection of camouflaged targets and has the potential to provide a new means for real-time multispectral sensing in complex scenes.<\/jats:p>","DOI":"10.3390\/rs13193949","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T21:26:20Z","timestamp":1633728380000},"page":"3949","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Camouflaged Target Detection Based on Snapshot Multispectral Imaging"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1322-1753","authenticated-orcid":false,"given":"Ying","family":"Shen","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9811-721X","authenticated-orcid":false,"given":"Jie","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}]},{"given":"Wenfu","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5058-0523","authenticated-orcid":false,"given":"Liqiong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}]},{"given":"Feng","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0829-5694","authenticated-orcid":false,"given":"Shu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhao, R., Shi, Z., Zou, Z., and Zhang, Z. (2019). Ensemble-based cascaded constrained energy minimization for hyperspectral target detection. Remote Sens., 11.","DOI":"10.3390\/rs11111310"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"B\u00e1rta, V., and Racek, F. (2017, January 11\u201312). Hyperspectral discrimination ofcamouflaged target. Proceedings of the SPIE 10432: SPIE Security + Defence: Target and Background Signatures III, Warsaw, Poland.","DOI":"10.1117\/12.2278578"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1109\/TGRS.2015.2456957","article-title":"Hierarchical Suppression Method for Hyperspectral Target Detection","volume":"54","author":"Zou","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","first-page":"79","article-title":"Hyperspectral image processing for automatic target detection applications","volume":"14","author":"Manolakis","year":"2003","journal-title":"Linc. Lab. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MSP.2013.2278915","article-title":"Detection algorithms in hyperspectral imaging systems: An overview of practical algorithms","volume":"31","author":"Manolakis","year":"2013","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Manolakis, D., Lockwood, R., Cooley, T., and Jacobson, J. (2009, January 13\u201316). Is there a best hyperspectral detection algorithm?. Proceedings of the SPIE 7334: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, Orlando, FL, USA.","DOI":"10.1117\/12.816917"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/79.974724","article-title":"Detection algorithms for hyperspectral imaging applications","volume":"19","author":"Manolakis","year":"2002","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_8","unstructured":"Yan, Q., Li, H., Wu, Y., Zhang, X., Wang, S., and Zhang, Q. (2019, January 12). Camouflage target detection based on short-wave infrared hyperspectral images. Proceedings of the SPIE 11023, The Fifth Symposium on Novel Optoelectronic Detection Technology and Application, Xi\u2019an, China."},{"key":"ref_9","unstructured":"Shi, G., Li, X., Huang, B., Hua, W., Guo, T., and Liu, X. (2015, January 5). Camouflage target reconnaissance based on hyperspectral imaging technology. Proceedings of the 2015 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, Beijing, China."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/S0034-4257(96)00080-6","article-title":"Mapping the distribution of mine tailings in the Coeur d\u2019Alene River Valley, Idaho, through the use of a constrained energy minimization technique","volume":"59","author":"Farrand","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s12524-016-0555-8","article-title":"Camouflage Detection Using MWIR Hyperspectral Images","volume":"45","author":"Kumar","year":"2016","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1109\/MSP.2016.2582378","article-title":"Computational Snapshot Multispectral Cameras Toward dynamic capture of the spectral world","volume":"33","author":"Cao","year":"2016","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6786","DOI":"10.1364\/AO.41.006786","article-title":"Wavelength band selection method for multispectral target detection","volume":"41","author":"Karlholm","year":"2002","journal-title":"Appl. Opt."},{"key":"ref_14","first-page":"5910","article-title":"Optimal Clustering Framework for Hyperspectral Band Selection","volume":"10","author":"Wang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1109\/TGRS.2015.2461653","article-title":"Unsupervised Band Selection Based on Evolutionary Multiobjective Optimization for Hyperspectral Images","volume":"54","author":"Gong","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4360","DOI":"10.1109\/TGRS.2019.2890848","article-title":"Scalable One-Pass Self-Representation Learning for Hyperspectral Band Selection","volume":"57","author":"Wei","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5975","DOI":"10.1080\/01431161.2010.512425","article-title":"Extended profiles with morphological attribute filters for the analysis of hyperspectral data","volume":"31","author":"Waske","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/TGRS.2004.842478","article-title":"Classification of hyperspectral data from urban areas based on extended morphological profiles","volume":"43","author":"Benediktsson","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.1109\/TGRS.2003.814625","article-title":"Classification and feature extraction for remote sensing images from urban areas based on morphological transformations","volume":"41","author":"Benediktsson","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3804","DOI":"10.1109\/TGRS.2008.922034","article-title":"Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles","volume":"46","author":"Fauvel","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kwan, C., Gribben, D., Ayhan, B., Bernabe, S., Plaza, A., and Selva, M. (2020). Improving Land Cover Classification Using Extended Multi-Attribute Profiles (EMAP) Enhanced Color, Near Infrared, and LiDAR Data. Remote Sens., 12.","DOI":"10.3390\/rs12091392"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kwan, C., Ayhan, B., Budavari, B., Lu, Y., Perez, D., Li, J., Bernabe, S., and Plaza, A. (2020). Deep Learning for Land Cover Classification Using Only a Few Bands. Remote Sens., 12.","DOI":"10.3390\/rs12122000"},{"key":"ref_23","first-page":"10","article-title":"The development of hyperspectral remote sensing and its threat to military equipment","volume":"23","author":"Zhang","year":"2008","journal-title":"Electro-Opt. Technol. Appl."},{"key":"ref_24","first-page":"166","article-title":"Hyperspectral imaging detection method for ground target camouflage features","volume":"6","author":"Liu","year":"2005","journal-title":"J. PLA Univ. Sci. Technol. (Nat. Sci. Ed.)"},{"key":"ref_25","first-page":"469","article-title":"Research on UV detection technology of snow camouflage materials based on reflection spectra and images","volume":"39","author":"Tian","year":"2017","journal-title":"Infrared Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1038\/nature14576","article-title":"A colloidal quantum dot spectrometer","volume":"523","author":"Bao","year":"2015","journal-title":"Nature"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1530","DOI":"10.1109\/TIP.2016.2523683","article-title":"Fourier spectral filter array for optimal multispectral imaging","volume":"25","author":"Jia","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Geelen, B., Carolina, B., Gonzalez, P., Tack, N., and Lambrechts, A. (2015). A tiny, VIS-NIR snapshot multispectral camera. Proc. SPIE\u2014Int. Soc. Opt. Eng., 9374.","DOI":"10.1117\/12.2077583"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Hu, J. (2008, January 12\u201314). Image segmentation based on 2D Otsu method with histogram analysis. Proceedings of the 2008 International Conference on Computer Science and Software Engineering, Wuhan, China.","DOI":"10.1109\/CSSE.2008.206"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.compeleceng.2016.09.019","article-title":"Image fusion based on object region detection and Non-Subsampled Contourlet Transform","volume":"62","author":"Meng","year":"2017","journal-title":"Comput. Electr. Eng."},{"key":"ref_32","unstructured":"Gonzalez, R.C., and Woods, R.E. (2007). Digital Image Processing, Prentice-Hall, Inc.. [3rd ed.]."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"27948","DOI":"10.1109\/ACCESS.2019.2901286","article-title":"Multi-Spectral Image Change Detection Based on Band Selection and Single-Band Iterative Weighting","volume":"7","author":"Ma","year":"2019","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5093","DOI":"10.1109\/TGRS.2017.2702197","article-title":"A Subpixel Target Detection Approach to Hyperspectral Image Classification","volume":"55","author":"Xue","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2405","DOI":"10.1109\/JSTARS.2014.2305441","article-title":"Hyperspectral and LiDAR data fusion: Outcome of the 2013 GRSS data fusion contest","volume":"7","author":"Debes","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1109\/MGRS.2019.2911100","article-title":"Hyperspectral band selection: A review","volume":"7","author":"Sun","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1876","DOI":"10.1109\/LGRS.2014.2312319","article-title":"CEM: More Bands, Better Performance","volume":"11","author":"Geng","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3949\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:08:49Z","timestamp":1760166529000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3949"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,2]]},"references-count":37,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13193949"],"URL":"https:\/\/doi.org\/10.3390\/rs13193949","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,2]]}}}