Low-Rank and Spectral-Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery

Sparse unmixing is an important technique for hyperspectral data analysis. Most sparse unmixing algorithms underutilize the spatial and spectral information of the hyperspectral image, which is unfavourable for the accuracy of endmember identification and abundance estimation. We propose a new spect...

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Autor principal:	Fan Li
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology T Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/9769a5f40dfe4481b37ecb06ca823295
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id	oai:doaj.org-article:9769a5f40dfe4481b37ecb06ca823295
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spelling	oai:doaj.org-article:9769a5f40dfe4481b37ecb06ca8232952021-11-08T02:36:26ZLow-Rank and Spectral-Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery1530-867710.1155/2021/9374908https://doaj.org/article/9769a5f40dfe4481b37ecb06ca8232952021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9374908https://doaj.org/toc/1530-8677Sparse unmixing is an important technique for hyperspectral data analysis. Most sparse unmixing algorithms underutilize the spatial and spectral information of the hyperspectral image, which is unfavourable for the accuracy of endmember identification and abundance estimation. We propose a new spectral unmixing method based on the low-rank representation model and spatial-weighted collaborative sparsity, aiming to exploit structural information in both the spatial and spectral domains for unmixing. The spatial weights are incorporated into the collaborative sparse regularization term to enhance the spatial continuity of the image. Meanwhile, the global low-rank constraint is employed to maintain the spatial low-dimensional structure of the image. The model is solved by the well-known alternating direction method of multiplier, in which the abundance coefficients and the spatial weights are updated iteratively in the inner and outer loops, respectively. Experimental results obtained from simulation and real data reveal the superior performance of the proposed algorithm on spectral unmixing.Fan LiHindawi-WileyarticleTechnologyTTelecommunicationTK5101-6720ENWireless Communications and Mobile Computing, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Telecommunication TK5101-6720
spellingShingle	Technology T Telecommunication TK5101-6720 Fan Li Low-Rank and Spectral-Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery
description	Sparse unmixing is an important technique for hyperspectral data analysis. Most sparse unmixing algorithms underutilize the spatial and spectral information of the hyperspectral image, which is unfavourable for the accuracy of endmember identification and abundance estimation. We propose a new spectral unmixing method based on the low-rank representation model and spatial-weighted collaborative sparsity, aiming to exploit structural information in both the spatial and spectral domains for unmixing. The spatial weights are incorporated into the collaborative sparse regularization term to enhance the spatial continuity of the image. Meanwhile, the global low-rank constraint is employed to maintain the spatial low-dimensional structure of the image. The model is solved by the well-known alternating direction method of multiplier, in which the abundance coefficients and the spatial weights are updated iteratively in the inner and outer loops, respectively. Experimental results obtained from simulation and real data reveal the superior performance of the proposed algorithm on spectral unmixing.
format	article
author	Fan Li
author_facet	Fan Li
author_sort	Fan Li
title	Low-Rank and Spectral-Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery
title_short	Low-Rank and Spectral-Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery
title_full	Low-Rank and Spectral-Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery
title_fullStr	Low-Rank and Spectral-Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery
title_full_unstemmed	Low-Rank and Spectral-Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery
title_sort	low-rank and spectral-spatial sparse unmixing for hyperspectral remote sensing imagery
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/9769a5f40dfe4481b37ecb06ca823295
work_keys_str_mv	AT fanli lowrankandspectralspatialsparseunmixingforhyperspectralremotesensingimagery
_version_	1718443119493513216

Low-Rank and Spectral-Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery

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