{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T01:55:45Z","timestamp":1772762145501,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,3,25]],"date-time":"2020-03-25T00:00:00Z","timestamp":1585094400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["61601077, 61971082, 61890964"],"award-info":[{"award-number":["61601077, 61971082, 61890964"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["3132019341"],"award-info":[{"award-number":["3132019341"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003512","name":"State Administration of Foreign Experts Affairs","doi-asserted-by":"publisher","award":["ZD20180073"],"award-info":[{"award-number":["ZD20180073"]}],"id":[{"id":"10.13039\/501100003512","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Compared to multi-spectral imagery, hyperspectral imagery has very high spectral resolution with abundant spectral information. In underwater target detection, hyperspectral technology can be advantageous in the sense of a poor underwater imaging environment, complex background, or protective mechanism of aquatic organisms. Due to high data redundancy, slow imaging speed, and long processing of hyperspectral imagery, a direct use of hyperspectral images in detecting targets cannot meet the needs of rapid detection of underwater targets. To resolve this issue, a fast, hyperspectral underwater target detection approach using band selection (BS) is proposed. It first develops a constrained-target optimal index factor (OIF) band selection (CTOIFBS) to select a band subset with spectral wavelengths specifically responding to the targets of interest. Then, an underwater spectral imaging system integrated with the best-selected band subset is constructed for underwater target image acquisition. Finally, a constrained energy minimization (CEM) target detection algorithm is used to detect the desired underwater targets. Experimental results demonstrate that the band subset selected by CTOIFBS is more effective in detecting underwater targets compared to the other three existing BS methods, uniform band selection (UBS), minimum variance band priority (MinV-BP), and minimum variance band priority with OIF (MinV-BP-OIF). In addition, the results also show that the acquisition and detection speed of the designed underwater spectral acquisition system using CTOIFBS can be significantly improved over the original underwater hyperspectral image system without BS.<\/jats:p>","DOI":"10.3390\/rs12071056","type":"journal-article","created":{"date-parts":[[2020,3,25]],"date-time":"2020-03-25T13:10:47Z","timestamp":1585141847000},"page":"1056","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Underwater Hyperspectral Target Detection with Band Selection"],"prefix":"10.3390","volume":"12","author":[{"given":"Xianping","family":"Fu","sequence":"first","affiliation":[{"name":"Information Science and Technology College, Dalian Maritime University, Dalian 116026, China"},{"name":"Peng Cheng Laboratory, Shengzhen 518000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodi","family":"Shang","sequence":"additional","affiliation":[{"name":"Information Science and Technology College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5870-6343","authenticated-orcid":false,"given":"Xudong","family":"Sun","sequence":"additional","affiliation":[{"name":"Information Science and Technology College, Dalian Maritime University, Dalian 116026, China"},{"name":"Peng Cheng Laboratory, Shengzhen 518000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4026-7450","authenticated-orcid":false,"given":"Haoyang","family":"Yu","sequence":"additional","affiliation":[{"name":"Information Science and Technology College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meiping","family":"Song","sequence":"additional","affiliation":[{"name":"Information Science and Technology College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5450-4891","authenticated-orcid":false,"given":"Chein-I","family":"Chang","sequence":"additional","affiliation":[{"name":"Information Science and Technology College, Dalian Maritime University, Dalian 116026, China"},{"name":"Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD 21250, USA"},{"name":"Department of Computer Science and Information Management, Providence University, Taichung 02912, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Christian, B., and Ronald, P. (2010). A fully automated method to detect and segment a manufactured object in an underwater color image. EURASIP J. Adv. Signal Process., 1\u201311.","DOI":"10.1155\/2010\/568092"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5664","DOI":"10.1109\/TIP.2016.2612882","article-title":"Underwater Image Enhancement by Dehazing With Minimum Information Loss and Histogram Distribution Prior","volume":"25","author":"Li","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2015","journal-title":"IEEE Transactions PAMI"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1109\/JOE.2014.2350751","article-title":"Underwater Optical Imaging: The Past, the Present, and the Prospects","volume":"40","author":"Jaffe","year":"2015","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1038\/scientificamerican0200-80","article-title":"Transparent animals","volume":"282","author":"Johnsen","year":"2000","journal-title":"Sci. Am."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1109\/29.60107","article-title":"Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution","volume":"38","author":"Reed","year":"1990","journal-title":"IEEE Trans. Acoust. Speech, Signal Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1109\/TGRS.2004.841487","article-title":"Kernel RX-algorithm: A nonlinear anomaly detector for hyperspectral imagery","volume":"43","author":"Kwon","year":"2005","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/TGRS.2004.839543","article-title":"Orthogonal subspace projection (OSP) revisited: A comprehensive study and analysis","volume":"43","author":"Chang","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","unstructured":"Chang, C.-I. (2003). Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Plenum Publishing Co."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.ifacol.2016.10.451","article-title":"The use of underwater hyperspectral imaging de-ployed on remotely operated vehicles\u2014Methods and applications","volume":"49","author":"Johnsen","year":"2016","journal-title":"IFAC-PapersOnLine"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sture, \u00d8., Ludvigsen, M., Soreide, F., and Aas, L.M.S. (2017). Autonomous underwater vehicles as a platform for underwater hyperspectral imaging. OCEANS Aberdeen, 1\u20138.","DOI":"10.1109\/OCEANSE.2017.8084995"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"011505","DOI":"10.1117\/1.2816113","article-title":"Retrieving key benthic cover types and bathymetry from hyperspectral imagery","volume":"1","author":"Klonowski","year":"2007","journal-title":"J. Appl. Remote. Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1016\/j.csr.2011.04.005","article-title":"Shallow water sub-strate mapping using hyperspectral remote sensing","volume":"31","author":"Fearns","year":"2011","journal-title":"Cont. Shelf Res."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Dierssen, H.M. (2013). Overview of hyperspectral remote sensing for mapping marine benthic habitats from airborne and underwater sensors. Opt. Eng. Appl., 88700.","DOI":"10.1117\/12.2026529"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.rse.2015.01.027","article-title":"Hyperspectral discrimination of floating mats of seagrass wrack and the macroalgae Sargassum in coastal waters of Greater Florida Bay using airborne remote sensing","volume":"167","author":"Dierssen","year":"2015","journal-title":"Remote. Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"12860","DOI":"10.1038\/s41598-018-31261-4","article-title":"Underwater hyperspectral imaging as an in situ taxonomic tool for deep-sea megafauna","volume":"8","author":"Dumke","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3909","DOI":"10.1109\/JSTARS.2016.2592987","article-title":"Development of a Low-Cost Hyperspectral Whiskbroom Imager Using an Optical Fiber Bundle, a Swing Mirror, and Compact Spectrometers","volume":"9","author":"Uto","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tegdan, J., Ekehaug, S., Hansen, I.M., Aas, L.M.S., Steen, K.J., Pettersen, R., Beuchel, F., and Camus, L. (2015). Underwater hyperspectral imaging for environmental mapping and monitoring of seabed habitats. OCEANS Genova, 1\u20136.","DOI":"10.1109\/OCEANS-Genova.2015.7271703"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1109\/TGRS.2004.830549","article-title":"Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries","volume":"42","author":"Keshava","year":"2004","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gleason, A.C.R., Reid, R.P., and Voss, K. (2007). Automated classification of underwater multispectral imagery for coral reef monitoring. OCEANS, 1\u20138.","DOI":"10.1109\/OCEANS.2007.4449394"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2631","DOI":"10.1109\/36.803411","article-title":"A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification","volume":"37","author":"Chang","year":"1999","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_23","unstructured":"Chavez, P.S., Berlin, G.L., and Sowers, L.B. (1982). Statistical method for selecting Landsat MSS ratios. J. Appl. Photogr. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1109\/LGRS.2010.2053516","article-title":"An Efficient Method for Supervised Hyperspectral Band Selection","volume":"8","author":"Yang","year":"2010","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/TIP.2016.2617462","article-title":"Discovering Diverse Subset for Unsupervised Hyperspectral Band Selection","volume":"26","author":"Yuan","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4887","DOI":"10.1109\/TGRS.2017.2681278","article-title":"Band Subset Selection for Anomaly Detection in Hyperspectral Imagery","volume":"55","author":"Wang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1080\/2150704X.2014.993482","article-title":"Band selection for target detection in hyperspectral imagery using sparse CEM","volume":"5","author":"Geng","year":"2014","journal-title":"Remote. Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6079","DOI":"10.1109\/TGRS.2019.2904264","article-title":"Constrained-Target BS for multiple-target detection","volume":"57","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1109\/TGRS.2003.819189","article-title":"Estimation of Number of Spectrally Distinct Signal Sources in Hyperspectral Imagery","volume":"42","author":"Chang","year":"2004","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2822","DOI":"10.1109\/TGRS.2017.2784372","article-title":"Band-Specified Virtual Dimensionality for Band Selection: An Orthogonal Subspace Projection Approach","volume":"56","author":"Yu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_31","first-page":"1285","article-title":"A review of virtual dimensionality for hyperspectral imagery","volume":"11","author":"Chang","year":"2018","journal-title":"IEEE J-STARS"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/JSEN.2009.2038120","article-title":"Multiple-parameter receiver operating characteristic analysis for signal detection and classification","volume":"10","author":"Chang","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chang, C.-I. (2013). Hyperspectral Data Processing: Algorithm Design and Analysis, Wiley.","DOI":"10.1002\/9781118269787"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Poor, H.V. (1994). An Introduction to Detection and Estimation Theory, Springer. [2nd ed.].","DOI":"10.1007\/978-1-4757-2341-0"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the mean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. Inf. Theory"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1056\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:11:31Z","timestamp":1760173891000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1056"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,25]]},"references-count":35,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["rs12071056"],"URL":"https:\/\/doi.org\/10.3390\/rs12071056","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,25]]}}}