Simultaneous Nonconvex Denoising and Unmixing for Hyperspectral Imaging

İnce, Taner; Dundar, Tugcan

doi:10.1109/access.2019.2938633

Cited by 10 publications

(4 citation statements)

References 58 publications

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“…The combination of unmixing and denoising has been treated in some recent studies. 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.…”

Section: Discussionmentioning

confidence: 99%

“…Some algorithms were developed to perform denoising and unmixing jointly in a unified framework, with the purpose of improving both. Both in [39,40], sparse representation frameworks were developed, where denoising and unmixing acted as constraints of each other. In most of the aforementioned studies, the performed experiments were small scale and rather anecdotal in nature.…”

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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How Hyperspectral Image Unmixing and Denoising Can Boost Each Other

et al. 2020

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“…Therefore, some techniques extract endmembers from class averages defined by ground truth information [83] or from a library of endmembers [84]. Some methods were developed for performing the denoising and unmixing in a unified framework for boosting the performance of each other [85], [86]. Recently, Block-Gaussian-Mixture Priors have been proposed for both hyperspectral denoising and inpainting [87].…”

Section: A Conventional Techniquesmentioning

confidence: 99%

Image Restoration for Remote Sensing: Overview and Toolbox

Rasti¹,

Chen²,

Dalsasso³

et al. 2021

Preprint

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This paper is under review in GRSM. Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (both active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering the true unknown image from the degraded observed image. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques in different paths according to each sensor type. This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration in the remote sensing community. We, therefore, provide a comprehensive, discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to investigate the vibrant topic of data restoration by supplying sufficient detail and references. Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community. The toolboxes are provided in https://github.com/ImageRestorationToolbox.

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“…Ince et. al propose a coupled unmixing and denoising method [17] to enhance the unmixing performance. Joint-sparse blocks and low-rank unmixing (JSpBLRU) [18] divides the abundance matrix into blocks and solves a joint-sparse regression problem to enhance the sparsity of each block.…”

Section: Introductionmentioning

confidence: 99%

Superpixel Weighted Low-rank and Sparse Approximation for Hyperspectral Unmixing

İnce¹,

Dundar²,

Kacmaz³

et al. 2022

Preprint

Self Cite

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We propose a superpixel weighted low-rank and sparse unmixing (SWLRSU) method for sparse unmixing. The proposed method consists of two steps. In the first step, we segment hyperspectral image into superpixels which are defined as the homogeneous regions with different shape and sizes according to the spatial structure. Then, an efficient method is proposed to obtain a spatial weight term using superpixels to capture the spatial structure of hyperspectral data. In the second step, we solve a superpixel guided low-rank and spatially weighted sparse approximation problem in which spatial weight term obtained in the first step is used as a weight term in sparsity promoting norm. This formulation exploits the spatial correlation of the pixels in the hyperspectral image efficiently, which yields satisfactory unmixing results. The experiments are conducted on simulated and real data sets to show the effectiveness of the proposed method.

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Simultaneous Nonconvex Denoising and Unmixing for Hyperspectral Imaging

Cited by 10 publications

References 58 publications

How Hyperspectral Image Unmixing and Denoising Can Boost Each Other

How Hyperspectral Image Unmixing and Denoising Can Boost Each Other

Image Restoration for Remote Sensing: Overview and Toolbox

Superpixel Weighted Low-rank and Sparse Approximation for Hyperspectral Unmixing

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