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

Lina Zhuang; Lianru Gao; Bing Zhang; José M. Bioucas-Dias

doi:10.1117/12.2280456

4 October 2017 Hyperspectral image denoising and anomaly detection based on low-rank and sparse representations

Lina Zhuang, Lianru Gao, Bing Zhang, José M. Bioucas-Dias

Proceedings Volume 10427, Image and Signal Processing for Remote Sensing XXIII; 104270M (2017) https://doi.org/10.1117/12.2280456
Event: SPIE Remote Sensing, 2017, Warsaw, Poland

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.

Conference Presentation

Citation Download Citation

Lina Zhuang, Lianru Gao, Bing Zhang, and José M. Bioucas-Dias "Hyperspectral image denoising and anomaly detection based on low-rank and sparse representations", Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 104270M (4 October 2017); https://doi.org/10.1117/12.2280456

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available