Linearly constrained minimum variance beamforming approach to target detection and classification for hyperspectral imagery

2003 ◽  
Author(s):  
Chein-I Chang ◽  
Hsuan Ren
Keyword(s):  
Target Detection ◽  
Hyperspectral Imagery ◽  
Minimum Variance ◽  
Linearly Constrained

scholarly journals Underwater Hyperspectral Target Detection with Band Selection

2020 ◽  
Vol 12 (7) ◽  
pp. 1056 ◽  
Author(s):  
Xianping Fu ◽  
Xiaodi Shang ◽  
Xudong Sun ◽  
Haoyang Yu ◽  
Meiping Song ◽  
...  
Keyword(s):  
Target Detection ◽  
Hyperspectral Image ◽  
Hyperspectral Imagery ◽  
Minimum Variance ◽  
High Spectral Resolution ◽  
Band Selection ◽  
Protective Mechanism ◽  
Underwater Target Detection ◽  
Underwater Targets ◽  
Underwater Target

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.

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