How Hyperspectral Image Unmixing and Denoising Can Boost Each Other
How Hyperspectral Image Unmixing and Denoising Can Boost Each Other
Rasti, B.; Koirala, B.; Scheunders, P.; Ghamisi, P.
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.
Keywords: hyperspectral image; unmixing; denoising; linear mixing model; low-rank model; noise reduction; abu
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How Hyperspectral Image Unmixing and Denoising Can Boost Each Other
ROBIS: 31912 HZDR-primary research data are used by this (Id 32304) publication
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Remote Sensing 12(2020)11, 1728
DOI: 10.3390/rs12111728
Cited 11 times in Scopus
Permalink: https://www.hzdr.de/publications/Publ-32304
Publ.-Id: 32304