Image and Signal Processing Methods

Improved band similarity-based hyperspectral imagery band selection for target detection

[+] Author Affiliations
Jing Zhang, Yan Cao, Li Zhuo, Chao Wang, Qianlan Zhou

Beijing University of Technology, Signal and Information Processing Laboratory, 100 Ping Le Yuan, Chao Yang District, Beijing 100124, China

J. Appl. Remote Sens. 9(1), 095091 (Feb 27, 2015). doi:10.1117/1.JRS.9.095091
History: Received August 5, 2014; Accepted February 3, 2015
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Abstract.  The high dimensionality of hyperspectral imagery is a huge challenge for remote sensing data processing. Band selection utilizes the most distinctive and informative band subset to reduce data dimensions. Although band selection can significantly alleviate the computational burden, the process itself may be time consuming because it needs to take all pixels into consideration, especially when the image spatial size is larger. An improved band similarity-based band selection method is proposed for hyperspectral imagery target detection, which includes four steps: (1) bad bands are removed by data preprocessing; (2) several selected pixels are used for band selection instead of using all the pixels to reduce the computational complexity; (3) hyperspectral imagery is analyzed for target detection; and (4) the number of selected bands is determined by adjusting the threshold of similarity metric, to ensure target detection operators have the best performance with selected bands. In the example, the well-known adaptive coherence estimator detector was used to evaluate the effectiveness of the proposed band selection method. The receiver operating characteristics curves were plotted to prove the proposed algorithm quantitatively. The experimental results show that our method can yield a better result in target detection than other band selection methods.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Jing Zhang ; Yan Cao ; Li Zhuo ; Chao Wang and Qianlan Zhou
"Improved band similarity-based hyperspectral imagery band selection for target detection", J. Appl. Remote Sens. 9(1), 095091 (Feb 27, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.095091


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