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Proceedings Paper

Hyperspectral image denoising and anomaly detection based on low-rank and sparse representations
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Paper Abstract

The very high spectral resolution of Hyperspectral Images (HSIs) enables the identification of materials with subtle differences and the extraction subpixel information. However, the increasing of spectral resolution often implies an increasing in the noise linked with the image formation process. This degradation mechanism limits the quality of extracted information and its potential applications. Since HSIs represent natural scenes and their spectral channels are highly correlated, they are characterized by a high level of self-similarity and are well approximated by low-rank representations. These characteristic underlies the state-of-the-art in HSI denoising. However, in presence of rare pixels, the denoising performance of those methods is not optimal and, in addition, it may compromise the future detection of those pixels. To address these hurdles, we introduce RhyDe (Robust hyperspectral Denoising), a powerful HSI denoiser, which implements explicit low-rank representation, promotes self-similarity, and, by using a form of collaborative sparsity, preserves rare pixels. The denoising and detection effectiveness of the proposed robust HSI denoiser is illustrated using semi-real data.

Paper Details

Date Published: 4 October 2017
PDF: 8 pages
Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 104270M (4 October 2017);
Show Author Affiliations
Lina Zhuang, Univ. de Lisboa (Portugal)
Lianru Gao, Institute of Remote Sensing and Digital Earth (China)
Bing Zhang, Institute of Remote Sensing and Digital Earth (China)
Univ. of Chinese Academy of Sciences (China)
José M. Bioucas-Dias, Univ. de Lisboa (Portugal)

Published in SPIE Proceedings Vol. 10427:
Image and Signal Processing for Remote Sensing XXIII
Lorenzo Bruzzone, Editor(s)

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