Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing

Hyperspectral unmixing is a crucial task for hyperspectral images (HSIs) processing, which estimates the proportions of constituent materials of a mixed pixel. Usually, the mixed pixels can be approximated using a linear mixing model. Since each material only occurs in a few pixels in real HSI, spar...
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Autor*in:	Lei Zhou [verfasserIn] Xueni Zhang [verfasserIn] Jianbo Wang [verfasserIn] Xiao Bai [verfasserIn] Lei Tong [verfasserIn] Liang Zhang [verfasserIn] Jun Zhou [verfasserIn] Edwin Hancock [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Hyperspectral unmixing (HU) linear mixing model (LMM) nonnegative matrix factorization (NMF) subspace structure similar graph

Übergeordnetes Werk:	In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing - IEEE, 2020, 13(2020), Seite 4257-4270
Übergeordnetes Werk:	volume:13 ; year:2020 ; pages:4257-4270

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/JSTARS.2020.3011257

Katalog-ID:	DOAJ006070264

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520			\|a Hyperspectral unmixing is a crucial task for hyperspectral images (HSIs) processing, which estimates the proportions of constituent materials of a mixed pixel. Usually, the mixed pixels can be approximated using a linear mixing model. Since each material only occurs in a few pixels in real HSI, sparse nonnegative matrix factorization (NMF), and its extensions are widely used as solutions. Some recent works assume that materials are distributed in certain structures, which can be added as constraints to sparse NMF model. However, they only consider the spatial distribution within a local neighborhood and define the distribution structure manually, while ignoring the real distribution of materials that is diverse in different images. In this article, we propose a new unmixing method that learns a subspace structure from the original image and incorporate it into the sparse NMF framework to promote unmixing performance. Based on the self-representation property of data points lying in the same subspace, the learned subspace structure can indicate the global similar graph of pixels that represents the real distribution of materials. Then the similar graph is used as a robust global spatial prior which is expected to be maintained in the decomposed abundance matrix. The experiments conducted on both simulated and real-world HSI datasets demonstrate the superior performance of our proposed method.
650		4	\|a Hyperspectral unmixing (HU)
650		4	\|a linear mixing model (LMM)
650		4	\|a nonnegative matrix factorization (NMF)
650		4	\|a subspace structure
650		4	\|a similar graph
653		0	\|a Ocean engineering
653		0	\|a Geophysics. Cosmic physics
700	0		\|a Xueni Zhang \|e verfasserin \|4 aut
700	0		\|a Jianbo Wang \|e verfasserin \|4 aut
700	0		\|a Xiao Bai \|e verfasserin \|4 aut
700	0		\|a Lei Tong \|e verfasserin \|4 aut
700	0		\|a Liang Zhang \|e verfasserin \|4 aut
700	0		\|a Jun Zhou \|e verfasserin \|4 aut
700	0		\|a Edwin Hancock \|e verfasserin \|4 aut
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allfields	10.1109/JSTARS.2020.3011257 doi (DE-627)DOAJ006070264 (DE-599)DOAJ142d3a368e77416fb8c2435de0cc03f3 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Lei Zhou verfasserin aut Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hyperspectral unmixing is a crucial task for hyperspectral images (HSIs) processing, which estimates the proportions of constituent materials of a mixed pixel. Usually, the mixed pixels can be approximated using a linear mixing model. Since each material only occurs in a few pixels in real HSI, sparse nonnegative matrix factorization (NMF), and its extensions are widely used as solutions. Some recent works assume that materials are distributed in certain structures, which can be added as constraints to sparse NMF model. However, they only consider the spatial distribution within a local neighborhood and define the distribution structure manually, while ignoring the real distribution of materials that is diverse in different images. In this article, we propose a new unmixing method that learns a subspace structure from the original image and incorporate it into the sparse NMF framework to promote unmixing performance. Based on the self-representation property of data points lying in the same subspace, the learned subspace structure can indicate the global similar graph of pixels that represents the real distribution of materials. Then the similar graph is used as a robust global spatial prior which is expected to be maintained in the decomposed abundance matrix. The experiments conducted on both simulated and real-world HSI datasets demonstrate the superior performance of our proposed method. Hyperspectral unmixing (HU) linear mixing model (LMM) nonnegative matrix factorization (NMF) subspace structure similar graph Ocean engineering Geophysics. Cosmic physics Xueni Zhang verfasserin aut Jianbo Wang verfasserin aut Xiao Bai verfasserin aut Lei Tong verfasserin aut Liang Zhang verfasserin aut Jun Zhou verfasserin aut Edwin Hancock verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 13(2020), Seite 4257-4270 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:13 year:2020 pages:4257-4270 https://doi.org/10.1109/JSTARS.2020.3011257 kostenfrei https://doaj.org/article/142d3a368e77416fb8c2435de0cc03f3 kostenfrei https://ieeexplore.ieee.org/document/9146211/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2020 4257-4270
spelling	10.1109/JSTARS.2020.3011257 doi (DE-627)DOAJ006070264 (DE-599)DOAJ142d3a368e77416fb8c2435de0cc03f3 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Lei Zhou verfasserin aut Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hyperspectral unmixing is a crucial task for hyperspectral images (HSIs) processing, which estimates the proportions of constituent materials of a mixed pixel. Usually, the mixed pixels can be approximated using a linear mixing model. Since each material only occurs in a few pixels in real HSI, sparse nonnegative matrix factorization (NMF), and its extensions are widely used as solutions. Some recent works assume that materials are distributed in certain structures, which can be added as constraints to sparse NMF model. However, they only consider the spatial distribution within a local neighborhood and define the distribution structure manually, while ignoring the real distribution of materials that is diverse in different images. In this article, we propose a new unmixing method that learns a subspace structure from the original image and incorporate it into the sparse NMF framework to promote unmixing performance. Based on the self-representation property of data points lying in the same subspace, the learned subspace structure can indicate the global similar graph of pixels that represents the real distribution of materials. Then the similar graph is used as a robust global spatial prior which is expected to be maintained in the decomposed abundance matrix. The experiments conducted on both simulated and real-world HSI datasets demonstrate the superior performance of our proposed method. Hyperspectral unmixing (HU) linear mixing model (LMM) nonnegative matrix factorization (NMF) subspace structure similar graph Ocean engineering Geophysics. Cosmic physics Xueni Zhang verfasserin aut Jianbo Wang verfasserin aut Xiao Bai verfasserin aut Lei Tong verfasserin aut Liang Zhang verfasserin aut Jun Zhou verfasserin aut Edwin Hancock verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 13(2020), Seite 4257-4270 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:13 year:2020 pages:4257-4270 https://doi.org/10.1109/JSTARS.2020.3011257 kostenfrei https://doaj.org/article/142d3a368e77416fb8c2435de0cc03f3 kostenfrei https://ieeexplore.ieee.org/document/9146211/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2020 4257-4270
allfields_unstemmed	10.1109/JSTARS.2020.3011257 doi (DE-627)DOAJ006070264 (DE-599)DOAJ142d3a368e77416fb8c2435de0cc03f3 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Lei Zhou verfasserin aut Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hyperspectral unmixing is a crucial task for hyperspectral images (HSIs) processing, which estimates the proportions of constituent materials of a mixed pixel. Usually, the mixed pixels can be approximated using a linear mixing model. Since each material only occurs in a few pixels in real HSI, sparse nonnegative matrix factorization (NMF), and its extensions are widely used as solutions. Some recent works assume that materials are distributed in certain structures, which can be added as constraints to sparse NMF model. However, they only consider the spatial distribution within a local neighborhood and define the distribution structure manually, while ignoring the real distribution of materials that is diverse in different images. In this article, we propose a new unmixing method that learns a subspace structure from the original image and incorporate it into the sparse NMF framework to promote unmixing performance. Based on the self-representation property of data points lying in the same subspace, the learned subspace structure can indicate the global similar graph of pixels that represents the real distribution of materials. Then the similar graph is used as a robust global spatial prior which is expected to be maintained in the decomposed abundance matrix. The experiments conducted on both simulated and real-world HSI datasets demonstrate the superior performance of our proposed method. Hyperspectral unmixing (HU) linear mixing model (LMM) nonnegative matrix factorization (NMF) subspace structure similar graph Ocean engineering Geophysics. Cosmic physics Xueni Zhang verfasserin aut Jianbo Wang verfasserin aut Xiao Bai verfasserin aut Lei Tong verfasserin aut Liang Zhang verfasserin aut Jun Zhou verfasserin aut Edwin Hancock verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 13(2020), Seite 4257-4270 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:13 year:2020 pages:4257-4270 https://doi.org/10.1109/JSTARS.2020.3011257 kostenfrei https://doaj.org/article/142d3a368e77416fb8c2435de0cc03f3 kostenfrei https://ieeexplore.ieee.org/document/9146211/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2020 4257-4270
allfieldsGer	10.1109/JSTARS.2020.3011257 doi (DE-627)DOAJ006070264 (DE-599)DOAJ142d3a368e77416fb8c2435de0cc03f3 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Lei Zhou verfasserin aut Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hyperspectral unmixing is a crucial task for hyperspectral images (HSIs) processing, which estimates the proportions of constituent materials of a mixed pixel. Usually, the mixed pixels can be approximated using a linear mixing model. Since each material only occurs in a few pixels in real HSI, sparse nonnegative matrix factorization (NMF), and its extensions are widely used as solutions. Some recent works assume that materials are distributed in certain structures, which can be added as constraints to sparse NMF model. However, they only consider the spatial distribution within a local neighborhood and define the distribution structure manually, while ignoring the real distribution of materials that is diverse in different images. In this article, we propose a new unmixing method that learns a subspace structure from the original image and incorporate it into the sparse NMF framework to promote unmixing performance. Based on the self-representation property of data points lying in the same subspace, the learned subspace structure can indicate the global similar graph of pixels that represents the real distribution of materials. Then the similar graph is used as a robust global spatial prior which is expected to be maintained in the decomposed abundance matrix. The experiments conducted on both simulated and real-world HSI datasets demonstrate the superior performance of our proposed method. Hyperspectral unmixing (HU) linear mixing model (LMM) nonnegative matrix factorization (NMF) subspace structure similar graph Ocean engineering Geophysics. Cosmic physics Xueni Zhang verfasserin aut Jianbo Wang verfasserin aut Xiao Bai verfasserin aut Lei Tong verfasserin aut Liang Zhang verfasserin aut Jun Zhou verfasserin aut Edwin Hancock verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 13(2020), Seite 4257-4270 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:13 year:2020 pages:4257-4270 https://doi.org/10.1109/JSTARS.2020.3011257 kostenfrei https://doaj.org/article/142d3a368e77416fb8c2435de0cc03f3 kostenfrei https://ieeexplore.ieee.org/document/9146211/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2020 4257-4270
allfieldsSound	10.1109/JSTARS.2020.3011257 doi (DE-627)DOAJ006070264 (DE-599)DOAJ142d3a368e77416fb8c2435de0cc03f3 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Lei Zhou verfasserin aut Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hyperspectral unmixing is a crucial task for hyperspectral images (HSIs) processing, which estimates the proportions of constituent materials of a mixed pixel. Usually, the mixed pixels can be approximated using a linear mixing model. Since each material only occurs in a few pixels in real HSI, sparse nonnegative matrix factorization (NMF), and its extensions are widely used as solutions. Some recent works assume that materials are distributed in certain structures, which can be added as constraints to sparse NMF model. However, they only consider the spatial distribution within a local neighborhood and define the distribution structure manually, while ignoring the real distribution of materials that is diverse in different images. In this article, we propose a new unmixing method that learns a subspace structure from the original image and incorporate it into the sparse NMF framework to promote unmixing performance. Based on the self-representation property of data points lying in the same subspace, the learned subspace structure can indicate the global similar graph of pixels that represents the real distribution of materials. Then the similar graph is used as a robust global spatial prior which is expected to be maintained in the decomposed abundance matrix. The experiments conducted on both simulated and real-world HSI datasets demonstrate the superior performance of our proposed method. Hyperspectral unmixing (HU) linear mixing model (LMM) nonnegative matrix factorization (NMF) subspace structure similar graph Ocean engineering Geophysics. Cosmic physics Xueni Zhang verfasserin aut Jianbo Wang verfasserin aut Xiao Bai verfasserin aut Lei Tong verfasserin aut Liang Zhang verfasserin aut Jun Zhou verfasserin aut Edwin Hancock verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 13(2020), Seite 4257-4270 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:13 year:2020 pages:4257-4270 https://doi.org/10.1109/JSTARS.2020.3011257 kostenfrei https://doaj.org/article/142d3a368e77416fb8c2435de0cc03f3 kostenfrei https://ieeexplore.ieee.org/document/9146211/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2020 4257-4270
language	English
source	In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13(2020), Seite 4257-4270 volume:13 year:2020 pages:4257-4270
sourceStr	In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13(2020), Seite 4257-4270 volume:13 year:2020 pages:4257-4270
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Hyperspectral unmixing (HU) linear mixing model (LMM) nonnegative matrix factorization (NMF) subspace structure similar graph Ocean engineering Geophysics. Cosmic physics
isfreeaccess_bool	true
container_title	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
authorswithroles_txt_mv	Lei Zhou @@aut@@ Xueni Zhang @@aut@@ Jianbo Wang @@aut@@ Xiao Bai @@aut@@ Lei Tong @@aut@@ Liang Zhang @@aut@@ Jun Zhou @@aut@@ Edwin Hancock @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	581732634
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language_de	englisch
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callnumber-first	T - Technology
author	Lei Zhou
spellingShingle	Lei Zhou misc TC1501-1800 misc QC801-809 misc Hyperspectral unmixing (HU) misc linear mixing model (LMM) misc nonnegative matrix factorization (NMF) misc subspace structure misc similar graph misc Ocean engineering misc Geophysics. Cosmic physics Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing
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topic_title	TC1501-1800 QC801-809 Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing Hyperspectral unmixing (HU) linear mixing model (LMM) nonnegative matrix factorization (NMF) subspace structure similar graph
topic	misc TC1501-1800 misc QC801-809 misc Hyperspectral unmixing (HU) misc linear mixing model (LMM) misc nonnegative matrix factorization (NMF) misc subspace structure misc similar graph misc Ocean engineering misc Geophysics. Cosmic physics
topic_unstemmed	misc TC1501-1800 misc QC801-809 misc Hyperspectral unmixing (HU) misc linear mixing model (LMM) misc nonnegative matrix factorization (NMF) misc subspace structure misc similar graph misc Ocean engineering misc Geophysics. Cosmic physics
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title	Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing
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title_full	Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing
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title_sort	subspace structure regularized nonnegative matrix factorization for hyperspectral unmixing
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title_auth	Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing
abstract	Hyperspectral unmixing is a crucial task for hyperspectral images (HSIs) processing, which estimates the proportions of constituent materials of a mixed pixel. Usually, the mixed pixels can be approximated using a linear mixing model. Since each material only occurs in a few pixels in real HSI, sparse nonnegative matrix factorization (NMF), and its extensions are widely used as solutions. Some recent works assume that materials are distributed in certain structures, which can be added as constraints to sparse NMF model. However, they only consider the spatial distribution within a local neighborhood and define the distribution structure manually, while ignoring the real distribution of materials that is diverse in different images. In this article, we propose a new unmixing method that learns a subspace structure from the original image and incorporate it into the sparse NMF framework to promote unmixing performance. Based on the self-representation property of data points lying in the same subspace, the learned subspace structure can indicate the global similar graph of pixels that represents the real distribution of materials. Then the similar graph is used as a robust global spatial prior which is expected to be maintained in the decomposed abundance matrix. The experiments conducted on both simulated and real-world HSI datasets demonstrate the superior performance of our proposed method.
abstractGer	Hyperspectral unmixing is a crucial task for hyperspectral images (HSIs) processing, which estimates the proportions of constituent materials of a mixed pixel. Usually, the mixed pixels can be approximated using a linear mixing model. Since each material only occurs in a few pixels in real HSI, sparse nonnegative matrix factorization (NMF), and its extensions are widely used as solutions. Some recent works assume that materials are distributed in certain structures, which can be added as constraints to sparse NMF model. However, they only consider the spatial distribution within a local neighborhood and define the distribution structure manually, while ignoring the real distribution of materials that is diverse in different images. In this article, we propose a new unmixing method that learns a subspace structure from the original image and incorporate it into the sparse NMF framework to promote unmixing performance. Based on the self-representation property of data points lying in the same subspace, the learned subspace structure can indicate the global similar graph of pixels that represents the real distribution of materials. Then the similar graph is used as a robust global spatial prior which is expected to be maintained in the decomposed abundance matrix. The experiments conducted on both simulated and real-world HSI datasets demonstrate the superior performance of our proposed method.
abstract_unstemmed	Hyperspectral unmixing is a crucial task for hyperspectral images (HSIs) processing, which estimates the proportions of constituent materials of a mixed pixel. Usually, the mixed pixels can be approximated using a linear mixing model. Since each material only occurs in a few pixels in real HSI, sparse nonnegative matrix factorization (NMF), and its extensions are widely used as solutions. Some recent works assume that materials are distributed in certain structures, which can be added as constraints to sparse NMF model. However, they only consider the spatial distribution within a local neighborhood and define the distribution structure manually, while ignoring the real distribution of materials that is diverse in different images. In this article, we propose a new unmixing method that learns a subspace structure from the original image and incorporate it into the sparse NMF framework to promote unmixing performance. Based on the self-representation property of data points lying in the same subspace, the learned subspace structure can indicate the global similar graph of pixels that represents the real distribution of materials. Then the similar graph is used as a robust global spatial prior which is expected to be maintained in the decomposed abundance matrix. The experiments conducted on both simulated and real-world HSI datasets demonstrate the superior performance of our proposed method.
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title_short	Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing
url	https://doi.org/10.1109/JSTARS.2020.3011257 https://doaj.org/article/142d3a368e77416fb8c2435de0cc03f3 https://ieeexplore.ieee.org/document/9146211/ https://doaj.org/toc/2151-1535
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Usually, the mixed pixels can be approximated using a linear mixing model. Since each material only occurs in a few pixels in real HSI, sparse nonnegative matrix factorization (NMF), and its extensions are widely used as solutions. Some recent works assume that materials are distributed in certain structures, which can be added as constraints to sparse NMF model. However, they only consider the spatial distribution within a local neighborhood and define the distribution structure manually, while ignoring the real distribution of materials that is diverse in different images. In this article, we propose a new unmixing method that learns a subspace structure from the original image and incorporate it into the sparse NMF framework to promote unmixing performance. Based on the self-representation property of data points lying in the same subspace, the learned subspace structure can indicate the global similar graph of pixels that represents the real distribution of materials. Then the similar graph is used as a robust global spatial prior which is expected to be maintained in the decomposed abundance matrix. The experiments conducted on both simulated and real-world HSI datasets demonstrate the superior performance of our proposed method.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hyperspectral unmixing (HU)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">linear mixing model (LMM)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">nonnegative matrix factorization (NMF)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">subspace structure</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">similar graph</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Ocean engineering</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Geophysics. Cosmic physics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xueni Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jianbo Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiao Bai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Lei Tong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Liang Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jun Zhou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Edwin Hancock</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</subfield><subfield code="d">IEEE, 2020</subfield><subfield code="g">13(2020), Seite 4257-4270</subfield><subfield code="w">(DE-627)581732634</subfield><subfield code="w">(DE-600)2457423-5</subfield><subfield code="x">21511535</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:13</subfield><subfield code="g">year:2020</subfield><subfield code="g">pages:4257-4270</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/JSTARS.2020.3011257</subfield><subfield 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Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing

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