Graph-Based Logarithmic Low-Rank Tensor Decomposition for the Fusion of Remotely Sensed Images

Hyperspectral images with high spatial resolution play an important role in material classification, change detection, and others. However, owing to the limitation of imaging sensors, it is difficult to directly acquire images with both high spatial resolution and high spectral resolution. Therefore...

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Autores principales:	Fei Ma, Shuai Huo, Feixia Yang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Graph regularization hyperspectral image (HSI) super-resolution image fusion low rank tensor decomposition Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
Acceso en línea:	https://doaj.org/article/daacdba225764451b380e0f01ca8cb1c
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id	oai:doaj.org-article:daacdba225764451b380e0f01ca8cb1c
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spelling	oai:doaj.org-article:daacdba225764451b380e0f01ca8cb1c2021-11-17T00:00:12ZGraph-Based Logarithmic Low-Rank Tensor Decomposition for the Fusion of Remotely Sensed Images2151-153510.1109/JSTARS.2021.3123466https://doaj.org/article/daacdba225764451b380e0f01ca8cb1c2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9591494/https://doaj.org/toc/2151-1535Hyperspectral images with high spatial resolution play an important role in material classification, change detection, and others. However, owing to the limitation of imaging sensors, it is difficult to directly acquire images with both high spatial resolution and high spectral resolution. Therefore, the fusion of remotely sensed images is an effective way to obtain high-resolution desired data, which is usually an ill-posed inverse problem and susceptible to noise corruption. To address these issues, a low-rank model based on tensor decomposition is proposed to fuse hyperspectral and multispectral images by incorporating graph regularization, in which the logarithmic low-rank function is utilized to suppress the small components for denoising. Furthermore, this article takes advantage of the local spatial similarity of remotely sensed images to enhance the reconstruction performance by constructing spatial graphs, and also promotes signature smoothing between adjacent endmember spectra using the neighborhood-based spectral graph regularization. Finally, a set of efficient solvers is carefully designed via alternating optimization for closed-from solutions and computational reduction, in which vector-matrix operators are adapted to solve the 3-D core tensor. Experimental tests on several real datasets illustrate that the proposed fusion method yields better reconstruction performance than the current state-of-the-art methods, and can significantly suppress noise at the same time.Fei MaShuai HuoFeixia YangIEEEarticleGraph regularizationhyperspectral image (HSI) super-resolutionimage fusionlow ranktensor decompositionOcean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11271-11286 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Graph regularization hyperspectral image (HSI) super-resolution image fusion low rank tensor decomposition Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
spellingShingle	Graph regularization hyperspectral image (HSI) super-resolution image fusion low rank tensor decomposition Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Fei Ma Shuai Huo Feixia Yang Graph-Based Logarithmic Low-Rank Tensor Decomposition for the Fusion of Remotely Sensed Images
description	Hyperspectral images with high spatial resolution play an important role in material classification, change detection, and others. However, owing to the limitation of imaging sensors, it is difficult to directly acquire images with both high spatial resolution and high spectral resolution. Therefore, the fusion of remotely sensed images is an effective way to obtain high-resolution desired data, which is usually an ill-posed inverse problem and susceptible to noise corruption. To address these issues, a low-rank model based on tensor decomposition is proposed to fuse hyperspectral and multispectral images by incorporating graph regularization, in which the logarithmic low-rank function is utilized to suppress the small components for denoising. Furthermore, this article takes advantage of the local spatial similarity of remotely sensed images to enhance the reconstruction performance by constructing spatial graphs, and also promotes signature smoothing between adjacent endmember spectra using the neighborhood-based spectral graph regularization. Finally, a set of efficient solvers is carefully designed via alternating optimization for closed-from solutions and computational reduction, in which vector-matrix operators are adapted to solve the 3-D core tensor. Experimental tests on several real datasets illustrate that the proposed fusion method yields better reconstruction performance than the current state-of-the-art methods, and can significantly suppress noise at the same time.
format	article
author	Fei Ma Shuai Huo Feixia Yang
author_facet	Fei Ma Shuai Huo Feixia Yang
author_sort	Fei Ma
title	Graph-Based Logarithmic Low-Rank Tensor Decomposition for the Fusion of Remotely Sensed Images
title_short	Graph-Based Logarithmic Low-Rank Tensor Decomposition for the Fusion of Remotely Sensed Images
title_full	Graph-Based Logarithmic Low-Rank Tensor Decomposition for the Fusion of Remotely Sensed Images
title_fullStr	Graph-Based Logarithmic Low-Rank Tensor Decomposition for the Fusion of Remotely Sensed Images
title_full_unstemmed	Graph-Based Logarithmic Low-Rank Tensor Decomposition for the Fusion of Remotely Sensed Images
title_sort	graph-based logarithmic low-rank tensor decomposition for the fusion of remotely sensed images
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/daacdba225764451b380e0f01ca8cb1c
work_keys_str_mv	AT feima graphbasedlogarithmiclowranktensordecompositionforthefusionofremotelysensedimages AT shuaihuo graphbasedlogarithmiclowranktensordecompositionforthefusionofremotelysensedimages AT feixiayang graphbasedlogarithmiclowranktensordecompositionforthefusionofremotelysensedimages
_version_	1718426078705352704

Graph-Based Logarithmic Low-Rank Tensor Decomposition for the Fusion of Remotely Sensed Images

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