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Hyperspectral band quality analysis based on dictionary representation
Author(s): Zhiqi Shen; Ning Zhang; Xiaoyan Luo; Xinzhong Zhu; Quanyuan Zhang
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Paper Abstract

Hyperspectral image can acquire hundreds of bands with wavelengths ranging from visible spectrum to infrared, and the rich discriminant information has led to the widespread applications in the military and civil fields. However, due to the influence of imaging devices, some bands are polluted by noise, which bring great inconvenience to subsequent processing. Therefore, in order to quickly and accurately find low quality and noisy bands in hyperspectral image, we propose a hyperspectral image quality analysis method based on band dictionary representation. Firstly, a representative band dictionary is constructed to accurately represent the dominant information in hyperspectral image. Then, the band subset dictionary is used to reconstruct all the remaining bands in the hyperspectral image and obtain the reconstruction coefficients of each band. Finally, representation error is calculated to analyze the quality of each band. By comparing the representation errors of the bands, the quality of each band can be estimated. Experimental results on three real-world hyperspectral images demonstrate the proposed method can effectively and quickly select low quality bands and noisy bands without any priors.

Paper Details

Date Published: 7 October 2019
PDF: 6 pages
Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 111551Z (7 October 2019); doi: 10.1117/12.2533884
Show Author Affiliations
Zhiqi Shen, Beihang Univ. (China)
Ning Zhang, Shanghai Aerospace Electronic Technology Institute (China)
Xiaoyan Luo, Beihang Univ. (China)
Xinzhong Zhu, Shanghai Aerospace Electronic Technology Institute (China)
Quanyuan Zhang, Shanghai Aerospace Electronic Technology Institute (China)


Published in SPIE Proceedings Vol. 11155:
Image and Signal Processing for Remote Sensing XXV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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