The Effect of Impulse Denoising on Geometric Based Hyperspectral Unmixing

Kocakusaklar, Bilal; Kahraman, Nihan

doi:10.18488/journal.63/2016.4.5/63.5.83.91

Cited by 1 publication

(2 citation statements)

References 6 publications

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“…Several research works [35,39,40] developed specific methods that could cope with noise and perform denoising and unmixing simultaneously. In [32][33][34], it was shown that denoising may help to boost the endmember extraction and the unmixing performance. On the other hand, it was observed that spectral unmixing had denoising performance, in particular when employing low-noise endmembers, obtained by prior knowledge in the form of class information [36] or library endmembers [38].…”

Section: Discussionmentioning

confidence: 99%

“…Although a large number of HSI denoising techniques have been developed, only a few of those works considered denoising as a preprocessing step for spectral unmixing. In [32], denoising was performed to improve the endmember extraction. In [33], the noisy and water absorption bands were denoised and included in the data to improve a sparse spectral unmixing technique.…”

Section: Introductionmentioning

confidence: 99%

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How Hyperspectral Image Unmixing and Denoising Can Boost Each Other

et al. 2020

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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.

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