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Journal of Applied Remote Sensing

Improved band similarity-based hyperspectral imagery band selection for target detection
Author(s): Jing Zhang; Yan Cao; Li Zhuo; Chao Wang; Qianlan Zhou
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Paper 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.

Paper Details

Date Published: 27 February 2015
PDF: 12 pages
J. Appl. Rem. Sens. 9(1) 095091 doi: 10.1117/1.JRS.9.095091
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Jing Zhang, Beijing Univ. of Technology (China)
Yan Cao, Beijing Univ. of Technology (China)
Li Zhuo, Beijing Univ. of Technology (China)
Chao Wang, Beijing Univ. of Technology (China)
Qianlan Zhou, Beijing Univ. of Technology (China)

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