Underwater Hyperspectral Target Detection with Band Selection

dc.contributor.authorFu, Xianping
dc.contributor.authorShang, Xiaodi
dc.contributor.authorSun, Xudong
dc.contributor.authorYu, Haoyang
dc.contributor.authorSong, Meiping
dc.contributor.authorChang, Chein-I
dc.date.accessioned2020-04-09T19:36:19Z
dc.date.available2020-04-09T19:36:19Z
dc.date.issued2020-03-25
dc.description.abstractCompared 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.en_US
dc.description.sponsorshipThis research was funded by the National Nature Science Foundation of China, grant number 61601077, 61971082, 61890964; Fundamental Research Funds for the Central Universities, grant number 3132019341; State Administration of Foreign Experts Affairs, grant number ZD20180073.en_US
dc.description.urihttps://www.mdpi.com/2072-4292/12/7/1056en_US
dc.format.extent21 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2x75y-qdtw
dc.identifier.citationFu, Xianping; Shang, Xiaodi; Sun, Xudong; Yu, Haoyang; Song, Meiping; Chang, Chein-I; Underwater Hyperspectral Target Detection with Band Selection; Remote Sensing. 2020, 12(7), 1056; https://www.mdpi.com/2072-4292/12/7/1056en_US
dc.identifier.urihttp://hdl.handle.net/11603/17913
dc.language.isoen_USen_US
dc.publisherMDPIen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsAttribution 4.0 International (CC BY 4.0)*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectUMBC Remote Sensing Signal and Image Processing Laboratoryen_US
dc.titleUnderwater Hyperspectral Target Detection with Band Selectionen_US
dc.typeTexten_US

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