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Open AccessFeature PaperArticle

How Hyperspectral Image Unmixing and Denoising Can Boost Each Other

1
Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Machine Learning Group, Chemnitzer Straße 40, 09599 Freiberg, Germany
2
Imec-Visionlab, University of Antwerp (CDE) Universiteitsplein 1, B-2610 Antwerp, Belgium
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(11), 1728; https://doi.org/10.3390/rs12111728
Received: 30 March 2020 / Revised: 25 May 2020 / Accepted: 26 May 2020 / Published: 28 May 2020
(This article belongs to the Special Issue Remote Sensing Applications of Image Denoising and Restoration)
Hyperspectral linear unmixing and denoising are highly related hyperspectral image (HSI) analysis tasks. In particular, with the assumption of Gaussian noise, the linear model assumed for the HSI in the case of low-rank denoising is often the same as the one used in HSI unmixing. However, the optimization criterion and the assumptions on the constraints are different. Additionally, noise reduction as a preprocessing step in hyperspectral data analysis is often ignored. The main goal of this paper is to study experimentally the influence of noise on the process of hyperspectral unmixing by: (1) investigating the effect of noise reduction as a preprocessing step on the performance of hyperspectral unmixing; (2) studying the relation between noise and different endmember selection strategies; (3) investigating the performance of HSI unmixing as an HSI denoiser; (4) comparing the denoising performance of spectral unmixing, state-of-the-art HSI denoising techniques, and the combination of both. All experiments are performed on simulated and real datasets. View Full-Text
Keywords: hyperspectral image; unmixing; denoising; linear mixing model; low-rank model; noise reduction; abundance estimation hyperspectral image; unmixing; denoising; linear mixing model; low-rank model; noise reduction; abundance estimation
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MDPI and ACS Style

Rasti, B.; Koirala, B.; Scheunders, P.; Ghamisi, P. How Hyperspectral Image Unmixing and Denoising Can Boost Each Other. Remote Sens. 2020, 12, 1728. https://doi.org/10.3390/rs12111728

AMA Style

Rasti B, Koirala B, Scheunders P, Ghamisi P. How Hyperspectral Image Unmixing and Denoising Can Boost Each Other. Remote Sensing. 2020; 12(11):1728. https://doi.org/10.3390/rs12111728

Chicago/Turabian Style

Rasti, Behnood; Koirala, Bikram; Scheunders, Paul; Ghamisi, Pedram. 2020. "How Hyperspectral Image Unmixing and Denoising Can Boost Each Other" Remote Sens. 12, no. 11: 1728. https://doi.org/10.3390/rs12111728

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