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Open AccessArticle

Underwater Hyperspectral Target Detection with Band Selection

1
Information Science and Technology College, Dalian Maritime University, Dalian 116026, China
2
Peng Cheng Laboratory, Shengzhen 518000, China
3
Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD 21250, USA
4
Department of Computer Science and Information Management, Providence University, Taichung 02912, Taiwan
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(7), 1056; https://doi.org/10.3390/rs12071056 (registering DOI)
Received: 24 January 2020 / Revised: 14 March 2020 / Accepted: 20 March 2020 / Published: 25 March 2020
(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)
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. View Full-Text
Keywords: constrained-target optimal index factor band selection (CTOIFBS); hyperspectral image; underwater spectral imaging system; underwater hyperspectral target detection; band selection (BS); constrained energy minimization (CEM) constrained-target optimal index factor band selection (CTOIFBS); hyperspectral image; underwater spectral imaging system; underwater hyperspectral target detection; band selection (BS); constrained energy minimization (CEM)
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MDPI and ACS Style

Fu, X.; Shang, X.; Sun, X.; Yu, H.; Song, M.; Chang, C.-I. Underwater Hyperspectral Target Detection with Band Selection. Remote Sens. 2020, 12, 1056.

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