An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing

With the improvement of spatial resolution of hyperspectral remote sensing images, the influence of spectral variability is gradually appearing in hyperspectral unmixing. The shortcomings of endmember extraction methods using a single spectrum to represent one type of material are revealed. To addre...
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Autor*in:	Chuanlong Ye [verfasserIn] Shanwei Liu [verfasserIn] Mingming Xu [verfasserIn] Bo Du [verfasserIn] Jianhua Wan [verfasserIn] Hui Sheng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	spectral variability endmember bundle spectral clustering

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 13(2021), 19, p 3941
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:19, p 3941

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs13193941

Katalog-ID:	DOAJ034584854

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allfields	10.3390/rs13193941 doi (DE-627)DOAJ034584854 (DE-599)DOAJc30aaeb0ef57469d9de4dc8db9e43910 DE-627 ger DE-627 rakwb eng Chuanlong Ye verfasserin aut An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the improvement of spatial resolution of hyperspectral remote sensing images, the influence of spectral variability is gradually appearing in hyperspectral unmixing. The shortcomings of endmember extraction methods using a single spectrum to represent one type of material are revealed. To address spectral variability for hyperspectral unmixing, a multiscale resampling endmember bundle extraction (MSREBE) method is proposed in this paper. There are four steps in the proposed endmember bundle extraction method: (1) boundary detection; (2) sub-images in multiscale generation; (3) endmember extraction from each sub-image; (4) stepwise most similar collection (SMSC) clustering. The SMSC clustering method is aimed at solving the problem in determining which endmember bundle the extracted endmembers belong to. Experiments carried on both a simulated dataset and real hyperspectral datasets show that the endmembers extracted by the proposed method are superior to those extracted by the compared methods, and the optimal results in abundance estimation are maintained. spectral variability endmember bundle spectral clustering Science Q Shanwei Liu verfasserin aut Mingming Xu verfasserin aut Bo Du verfasserin aut Jianhua Wan verfasserin aut Hui Sheng verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 19, p 3941 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:19, p 3941 https://doi.org/10.3390/rs13193941 kostenfrei https://doaj.org/article/c30aaeb0ef57469d9de4dc8db9e43910 kostenfrei https://www.mdpi.com/2072-4292/13/19/3941 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2021 19, p 3941
spelling	10.3390/rs13193941 doi (DE-627)DOAJ034584854 (DE-599)DOAJc30aaeb0ef57469d9de4dc8db9e43910 DE-627 ger DE-627 rakwb eng Chuanlong Ye verfasserin aut An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the improvement of spatial resolution of hyperspectral remote sensing images, the influence of spectral variability is gradually appearing in hyperspectral unmixing. The shortcomings of endmember extraction methods using a single spectrum to represent one type of material are revealed. To address spectral variability for hyperspectral unmixing, a multiscale resampling endmember bundle extraction (MSREBE) method is proposed in this paper. There are four steps in the proposed endmember bundle extraction method: (1) boundary detection; (2) sub-images in multiscale generation; (3) endmember extraction from each sub-image; (4) stepwise most similar collection (SMSC) clustering. The SMSC clustering method is aimed at solving the problem in determining which endmember bundle the extracted endmembers belong to. Experiments carried on both a simulated dataset and real hyperspectral datasets show that the endmembers extracted by the proposed method are superior to those extracted by the compared methods, and the optimal results in abundance estimation are maintained. spectral variability endmember bundle spectral clustering Science Q Shanwei Liu verfasserin aut Mingming Xu verfasserin aut Bo Du verfasserin aut Jianhua Wan verfasserin aut Hui Sheng verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 19, p 3941 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:19, p 3941 https://doi.org/10.3390/rs13193941 kostenfrei https://doaj.org/article/c30aaeb0ef57469d9de4dc8db9e43910 kostenfrei https://www.mdpi.com/2072-4292/13/19/3941 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2021 19, p 3941
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allfieldsGer	10.3390/rs13193941 doi (DE-627)DOAJ034584854 (DE-599)DOAJc30aaeb0ef57469d9de4dc8db9e43910 DE-627 ger DE-627 rakwb eng Chuanlong Ye verfasserin aut An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the improvement of spatial resolution of hyperspectral remote sensing images, the influence of spectral variability is gradually appearing in hyperspectral unmixing. The shortcomings of endmember extraction methods using a single spectrum to represent one type of material are revealed. To address spectral variability for hyperspectral unmixing, a multiscale resampling endmember bundle extraction (MSREBE) method is proposed in this paper. There are four steps in the proposed endmember bundle extraction method: (1) boundary detection; (2) sub-images in multiscale generation; (3) endmember extraction from each sub-image; (4) stepwise most similar collection (SMSC) clustering. The SMSC clustering method is aimed at solving the problem in determining which endmember bundle the extracted endmembers belong to. Experiments carried on both a simulated dataset and real hyperspectral datasets show that the endmembers extracted by the proposed method are superior to those extracted by the compared methods, and the optimal results in abundance estimation are maintained. spectral variability endmember bundle spectral clustering Science Q Shanwei Liu verfasserin aut Mingming Xu verfasserin aut Bo Du verfasserin aut Jianhua Wan verfasserin aut Hui Sheng verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 19, p 3941 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:19, p 3941 https://doi.org/10.3390/rs13193941 kostenfrei https://doaj.org/article/c30aaeb0ef57469d9de4dc8db9e43910 kostenfrei https://www.mdpi.com/2072-4292/13/19/3941 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2021 19, p 3941
allfieldsSound	10.3390/rs13193941 doi (DE-627)DOAJ034584854 (DE-599)DOAJc30aaeb0ef57469d9de4dc8db9e43910 DE-627 ger DE-627 rakwb eng Chuanlong Ye verfasserin aut An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the improvement of spatial resolution of hyperspectral remote sensing images, the influence of spectral variability is gradually appearing in hyperspectral unmixing. The shortcomings of endmember extraction methods using a single spectrum to represent one type of material are revealed. To address spectral variability for hyperspectral unmixing, a multiscale resampling endmember bundle extraction (MSREBE) method is proposed in this paper. There are four steps in the proposed endmember bundle extraction method: (1) boundary detection; (2) sub-images in multiscale generation; (3) endmember extraction from each sub-image; (4) stepwise most similar collection (SMSC) clustering. The SMSC clustering method is aimed at solving the problem in determining which endmember bundle the extracted endmembers belong to. Experiments carried on both a simulated dataset and real hyperspectral datasets show that the endmembers extracted by the proposed method are superior to those extracted by the compared methods, and the optimal results in abundance estimation are maintained. spectral variability endmember bundle spectral clustering Science Q Shanwei Liu verfasserin aut Mingming Xu verfasserin aut Bo Du verfasserin aut Jianhua Wan verfasserin aut Hui Sheng verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 19, p 3941 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:19, p 3941 https://doi.org/10.3390/rs13193941 kostenfrei https://doaj.org/article/c30aaeb0ef57469d9de4dc8db9e43910 kostenfrei https://www.mdpi.com/2072-4292/13/19/3941 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2021 19, p 3941
language	English
source	In Remote Sensing 13(2021), 19, p 3941 volume:13 year:2021 number:19, p 3941
sourceStr	In Remote Sensing 13(2021), 19, p 3941 volume:13 year:2021 number:19, p 3941
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topic_facet	spectral variability endmember bundle spectral clustering Science Q
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authorswithroles_txt_mv	Chuanlong Ye @@aut@@ Shanwei Liu @@aut@@ Mingming Xu @@aut@@ Bo Du @@aut@@ Jianhua Wan @@aut@@ Hui Sheng @@aut@@
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author	Chuanlong Ye
spellingShingle	Chuanlong Ye misc spectral variability misc endmember bundle misc spectral clustering misc Science misc Q An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing
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topic_title	An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing spectral variability endmember bundle spectral clustering
topic	misc spectral variability misc endmember bundle misc spectral clustering misc Science misc Q
topic_unstemmed	misc spectral variability misc endmember bundle misc spectral clustering misc Science misc Q
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title	An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing
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title_full	An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing
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title_sort	endmember bundle extraction method based on multiscale sampling to address spectral variability for hyperspectral unmixing
title_auth	An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing
abstract	With the improvement of spatial resolution of hyperspectral remote sensing images, the influence of spectral variability is gradually appearing in hyperspectral unmixing. The shortcomings of endmember extraction methods using a single spectrum to represent one type of material are revealed. To address spectral variability for hyperspectral unmixing, a multiscale resampling endmember bundle extraction (MSREBE) method is proposed in this paper. There are four steps in the proposed endmember bundle extraction method: (1) boundary detection; (2) sub-images in multiscale generation; (3) endmember extraction from each sub-image; (4) stepwise most similar collection (SMSC) clustering. The SMSC clustering method is aimed at solving the problem in determining which endmember bundle the extracted endmembers belong to. Experiments carried on both a simulated dataset and real hyperspectral datasets show that the endmembers extracted by the proposed method are superior to those extracted by the compared methods, and the optimal results in abundance estimation are maintained.
abstractGer	With the improvement of spatial resolution of hyperspectral remote sensing images, the influence of spectral variability is gradually appearing in hyperspectral unmixing. The shortcomings of endmember extraction methods using a single spectrum to represent one type of material are revealed. To address spectral variability for hyperspectral unmixing, a multiscale resampling endmember bundle extraction (MSREBE) method is proposed in this paper. There are four steps in the proposed endmember bundle extraction method: (1) boundary detection; (2) sub-images in multiscale generation; (3) endmember extraction from each sub-image; (4) stepwise most similar collection (SMSC) clustering. The SMSC clustering method is aimed at solving the problem in determining which endmember bundle the extracted endmembers belong to. Experiments carried on both a simulated dataset and real hyperspectral datasets show that the endmembers extracted by the proposed method are superior to those extracted by the compared methods, and the optimal results in abundance estimation are maintained.
abstract_unstemmed	With the improvement of spatial resolution of hyperspectral remote sensing images, the influence of spectral variability is gradually appearing in hyperspectral unmixing. The shortcomings of endmember extraction methods using a single spectrum to represent one type of material are revealed. To address spectral variability for hyperspectral unmixing, a multiscale resampling endmember bundle extraction (MSREBE) method is proposed in this paper. There are four steps in the proposed endmember bundle extraction method: (1) boundary detection; (2) sub-images in multiscale generation; (3) endmember extraction from each sub-image; (4) stepwise most similar collection (SMSC) clustering. The SMSC clustering method is aimed at solving the problem in determining which endmember bundle the extracted endmembers belong to. Experiments carried on both a simulated dataset and real hyperspectral datasets show that the endmembers extracted by the proposed method are superior to those extracted by the compared methods, and the optimal results in abundance estimation are maintained.
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title_short	An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing
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