A Sparse Topic Relaxion and Group Clustering Model for Hyperspectral Unmixing

Hyperspectral unmixing (HU) has been a hot research topic in the field of hyperspectral remote sensing. In recent years, the employment of the probabilistic topic model to acquire the latent topics of hyperspectral images has been an effective method for spectral unmixing. However, such methods fail...
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Autor*in:	Qiqi Zhu [verfasserIn] Linlin Wang [verfasserIn] Wen Zeng [verfasserIn] Qingfeng Guan [verfasserIn] Zhen Hu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Abundance endmember group clustering hyperspectral unmixing (HU) sparse topic relaxion

Übergeordnetes Werk:	In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing - IEEE, 2020, 14(2021), Seite 4014-4027
Übergeordnetes Werk:	volume:14 ; year:2021 ; pages:4014-4027

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/JSTARS.2021.3069574

Katalog-ID:	DOAJ067714153

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520			\|a Hyperspectral unmixing (HU) has been a hot research topic in the field of hyperspectral remote sensing. In recent years, the employment of the probabilistic topic model to acquire the latent topics of hyperspectral images has been an effective method for spectral unmixing. However, such methods fail to fully exploit the potential of topic models in uncovering image semantics, and they need extra sparsity constraints, which greatly increases the complexity of the model. To solve these problems, a sparse topic relaxion and group clustering model for HU (STRGC) is proposed. In STRGC, the sparse prior constraints implied by the sparse topic model are introduced, which means that the sparse characteristics of the model are used to capture the semantic representation of the spectrum. Through the relaxation of the model, the possible spectral representations of ground features can be obtained, and this further alleviates the influence caused by endmember variability on the accuracy of the unmixing process. Then, fuzzy clustering is used to locate the position of the endmember quickly and accurately. Furthermore, unmixing models with different characteristics are united to alleviate the ill-posed nature of the model, thereby improving the fractional abundance. Experimental results obtained with one simulated dataset and three well-known real hyperspectral datasets confirm the effectiveness and advantages of the proposed method.
650		4	\|a Abundance
650		4	\|a endmember
650		4	\|a group clustering
650		4	\|a hyperspectral unmixing (HU)
650		4	\|a sparse topic relaxion
653		0	\|a Ocean engineering
653		0	\|a Geophysics. Cosmic physics
700	0		\|a Linlin Wang \|e verfasserin \|4 aut
700	0		\|a Wen Zeng \|e verfasserin \|4 aut
700	0		\|a Qingfeng Guan \|e verfasserin \|4 aut
700	0		\|a Zhen Hu \|e verfasserin \|4 aut
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allfields	10.1109/JSTARS.2021.3069574 doi (DE-627)DOAJ067714153 (DE-599)DOAJd944df572b614ed0886667a92d00ef64 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Qiqi Zhu verfasserin aut A Sparse Topic Relaxion and Group Clustering Model for Hyperspectral Unmixing 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hyperspectral unmixing (HU) has been a hot research topic in the field of hyperspectral remote sensing. In recent years, the employment of the probabilistic topic model to acquire the latent topics of hyperspectral images has been an effective method for spectral unmixing. However, such methods fail to fully exploit the potential of topic models in uncovering image semantics, and they need extra sparsity constraints, which greatly increases the complexity of the model. To solve these problems, a sparse topic relaxion and group clustering model for HU (STRGC) is proposed. In STRGC, the sparse prior constraints implied by the sparse topic model are introduced, which means that the sparse characteristics of the model are used to capture the semantic representation of the spectrum. Through the relaxation of the model, the possible spectral representations of ground features can be obtained, and this further alleviates the influence caused by endmember variability on the accuracy of the unmixing process. Then, fuzzy clustering is used to locate the position of the endmember quickly and accurately. Furthermore, unmixing models with different characteristics are united to alleviate the ill-posed nature of the model, thereby improving the fractional abundance. Experimental results obtained with one simulated dataset and three well-known real hyperspectral datasets confirm the effectiveness and advantages of the proposed method. Abundance endmember group clustering hyperspectral unmixing (HU) sparse topic relaxion Ocean engineering Geophysics. Cosmic physics Linlin Wang verfasserin aut Wen Zeng verfasserin aut Qingfeng Guan verfasserin aut Zhen Hu verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 14(2021), Seite 4014-4027 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:14 year:2021 pages:4014-4027 https://doi.org/10.1109/JSTARS.2021.3069574 kostenfrei https://doaj.org/article/d944df572b614ed0886667a92d00ef64 kostenfrei https://ieeexplore.ieee.org/document/9390184/ 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 14 2021 4014-4027
spelling	10.1109/JSTARS.2021.3069574 doi (DE-627)DOAJ067714153 (DE-599)DOAJd944df572b614ed0886667a92d00ef64 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Qiqi Zhu verfasserin aut A Sparse Topic Relaxion and Group Clustering Model for Hyperspectral Unmixing 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hyperspectral unmixing (HU) has been a hot research topic in the field of hyperspectral remote sensing. In recent years, the employment of the probabilistic topic model to acquire the latent topics of hyperspectral images has been an effective method for spectral unmixing. However, such methods fail to fully exploit the potential of topic models in uncovering image semantics, and they need extra sparsity constraints, which greatly increases the complexity of the model. To solve these problems, a sparse topic relaxion and group clustering model for HU (STRGC) is proposed. In STRGC, the sparse prior constraints implied by the sparse topic model are introduced, which means that the sparse characteristics of the model are used to capture the semantic representation of the spectrum. Through the relaxation of the model, the possible spectral representations of ground features can be obtained, and this further alleviates the influence caused by endmember variability on the accuracy of the unmixing process. Then, fuzzy clustering is used to locate the position of the endmember quickly and accurately. Furthermore, unmixing models with different characteristics are united to alleviate the ill-posed nature of the model, thereby improving the fractional abundance. Experimental results obtained with one simulated dataset and three well-known real hyperspectral datasets confirm the effectiveness and advantages of the proposed method. Abundance endmember group clustering hyperspectral unmixing (HU) sparse topic relaxion Ocean engineering Geophysics. Cosmic physics Linlin Wang verfasserin aut Wen Zeng verfasserin aut Qingfeng Guan verfasserin aut Zhen Hu verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 14(2021), Seite 4014-4027 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:14 year:2021 pages:4014-4027 https://doi.org/10.1109/JSTARS.2021.3069574 kostenfrei https://doaj.org/article/d944df572b614ed0886667a92d00ef64 kostenfrei https://ieeexplore.ieee.org/document/9390184/ 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 14 2021 4014-4027
allfields_unstemmed	10.1109/JSTARS.2021.3069574 doi (DE-627)DOAJ067714153 (DE-599)DOAJd944df572b614ed0886667a92d00ef64 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Qiqi Zhu verfasserin aut A Sparse Topic Relaxion and Group Clustering Model for Hyperspectral Unmixing 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hyperspectral unmixing (HU) has been a hot research topic in the field of hyperspectral remote sensing. In recent years, the employment of the probabilistic topic model to acquire the latent topics of hyperspectral images has been an effective method for spectral unmixing. However, such methods fail to fully exploit the potential of topic models in uncovering image semantics, and they need extra sparsity constraints, which greatly increases the complexity of the model. To solve these problems, a sparse topic relaxion and group clustering model for HU (STRGC) is proposed. In STRGC, the sparse prior constraints implied by the sparse topic model are introduced, which means that the sparse characteristics of the model are used to capture the semantic representation of the spectrum. Through the relaxation of the model, the possible spectral representations of ground features can be obtained, and this further alleviates the influence caused by endmember variability on the accuracy of the unmixing process. Then, fuzzy clustering is used to locate the position of the endmember quickly and accurately. Furthermore, unmixing models with different characteristics are united to alleviate the ill-posed nature of the model, thereby improving the fractional abundance. Experimental results obtained with one simulated dataset and three well-known real hyperspectral datasets confirm the effectiveness and advantages of the proposed method. Abundance endmember group clustering hyperspectral unmixing (HU) sparse topic relaxion Ocean engineering Geophysics. Cosmic physics Linlin Wang verfasserin aut Wen Zeng verfasserin aut Qingfeng Guan verfasserin aut Zhen Hu verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 14(2021), Seite 4014-4027 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:14 year:2021 pages:4014-4027 https://doi.org/10.1109/JSTARS.2021.3069574 kostenfrei https://doaj.org/article/d944df572b614ed0886667a92d00ef64 kostenfrei https://ieeexplore.ieee.org/document/9390184/ 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 14 2021 4014-4027
allfieldsGer	10.1109/JSTARS.2021.3069574 doi (DE-627)DOAJ067714153 (DE-599)DOAJd944df572b614ed0886667a92d00ef64 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Qiqi Zhu verfasserin aut A Sparse Topic Relaxion and Group Clustering Model for Hyperspectral Unmixing 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hyperspectral unmixing (HU) has been a hot research topic in the field of hyperspectral remote sensing. In recent years, the employment of the probabilistic topic model to acquire the latent topics of hyperspectral images has been an effective method for spectral unmixing. However, such methods fail to fully exploit the potential of topic models in uncovering image semantics, and they need extra sparsity constraints, which greatly increases the complexity of the model. To solve these problems, a sparse topic relaxion and group clustering model for HU (STRGC) is proposed. In STRGC, the sparse prior constraints implied by the sparse topic model are introduced, which means that the sparse characteristics of the model are used to capture the semantic representation of the spectrum. Through the relaxation of the model, the possible spectral representations of ground features can be obtained, and this further alleviates the influence caused by endmember variability on the accuracy of the unmixing process. Then, fuzzy clustering is used to locate the position of the endmember quickly and accurately. Furthermore, unmixing models with different characteristics are united to alleviate the ill-posed nature of the model, thereby improving the fractional abundance. Experimental results obtained with one simulated dataset and three well-known real hyperspectral datasets confirm the effectiveness and advantages of the proposed method. Abundance endmember group clustering hyperspectral unmixing (HU) sparse topic relaxion Ocean engineering Geophysics. Cosmic physics Linlin Wang verfasserin aut Wen Zeng verfasserin aut Qingfeng Guan verfasserin aut Zhen Hu verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 14(2021), Seite 4014-4027 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:14 year:2021 pages:4014-4027 https://doi.org/10.1109/JSTARS.2021.3069574 kostenfrei https://doaj.org/article/d944df572b614ed0886667a92d00ef64 kostenfrei https://ieeexplore.ieee.org/document/9390184/ 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 14 2021 4014-4027
allfieldsSound	10.1109/JSTARS.2021.3069574 doi (DE-627)DOAJ067714153 (DE-599)DOAJd944df572b614ed0886667a92d00ef64 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Qiqi Zhu verfasserin aut A Sparse Topic Relaxion and Group Clustering Model for Hyperspectral Unmixing 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hyperspectral unmixing (HU) has been a hot research topic in the field of hyperspectral remote sensing. In recent years, the employment of the probabilistic topic model to acquire the latent topics of hyperspectral images has been an effective method for spectral unmixing. However, such methods fail to fully exploit the potential of topic models in uncovering image semantics, and they need extra sparsity constraints, which greatly increases the complexity of the model. To solve these problems, a sparse topic relaxion and group clustering model for HU (STRGC) is proposed. In STRGC, the sparse prior constraints implied by the sparse topic model are introduced, which means that the sparse characteristics of the model are used to capture the semantic representation of the spectrum. Through the relaxation of the model, the possible spectral representations of ground features can be obtained, and this further alleviates the influence caused by endmember variability on the accuracy of the unmixing process. Then, fuzzy clustering is used to locate the position of the endmember quickly and accurately. Furthermore, unmixing models with different characteristics are united to alleviate the ill-posed nature of the model, thereby improving the fractional abundance. Experimental results obtained with one simulated dataset and three well-known real hyperspectral datasets confirm the effectiveness and advantages of the proposed method. Abundance endmember group clustering hyperspectral unmixing (HU) sparse topic relaxion Ocean engineering Geophysics. Cosmic physics Linlin Wang verfasserin aut Wen Zeng verfasserin aut Qingfeng Guan verfasserin aut Zhen Hu verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 14(2021), Seite 4014-4027 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:14 year:2021 pages:4014-4027 https://doi.org/10.1109/JSTARS.2021.3069574 kostenfrei https://doaj.org/article/d944df572b614ed0886667a92d00ef64 kostenfrei https://ieeexplore.ieee.org/document/9390184/ 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 14 2021 4014-4027
language	English
source	In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14(2021), Seite 4014-4027 volume:14 year:2021 pages:4014-4027
sourceStr	In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14(2021), Seite 4014-4027 volume:14 year:2021 pages:4014-4027
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container_title	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
authorswithroles_txt_mv	Qiqi Zhu @@aut@@ Linlin Wang @@aut@@ Wen Zeng @@aut@@ Qingfeng Guan @@aut@@ Zhen Hu @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
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callnumber-first	T - Technology
author	Qiqi Zhu
spellingShingle	Qiqi Zhu misc TC1501-1800 misc QC801-809 misc Abundance misc endmember misc group clustering misc hyperspectral unmixing (HU) misc sparse topic relaxion misc Ocean engineering misc Geophysics. Cosmic physics A Sparse Topic Relaxion and Group Clustering Model for Hyperspectral Unmixing
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title	A Sparse Topic Relaxion and Group Clustering Model for Hyperspectral Unmixing
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title_sort	sparse topic relaxion and group clustering model for hyperspectral unmixing
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title_auth	A Sparse Topic Relaxion and Group Clustering Model for Hyperspectral Unmixing
abstract	Hyperspectral unmixing (HU) has been a hot research topic in the field of hyperspectral remote sensing. In recent years, the employment of the probabilistic topic model to acquire the latent topics of hyperspectral images has been an effective method for spectral unmixing. However, such methods fail to fully exploit the potential of topic models in uncovering image semantics, and they need extra sparsity constraints, which greatly increases the complexity of the model. To solve these problems, a sparse topic relaxion and group clustering model for HU (STRGC) is proposed. In STRGC, the sparse prior constraints implied by the sparse topic model are introduced, which means that the sparse characteristics of the model are used to capture the semantic representation of the spectrum. Through the relaxation of the model, the possible spectral representations of ground features can be obtained, and this further alleviates the influence caused by endmember variability on the accuracy of the unmixing process. Then, fuzzy clustering is used to locate the position of the endmember quickly and accurately. Furthermore, unmixing models with different characteristics are united to alleviate the ill-posed nature of the model, thereby improving the fractional abundance. Experimental results obtained with one simulated dataset and three well-known real hyperspectral datasets confirm the effectiveness and advantages of the proposed method.
abstractGer	Hyperspectral unmixing (HU) has been a hot research topic in the field of hyperspectral remote sensing. In recent years, the employment of the probabilistic topic model to acquire the latent topics of hyperspectral images has been an effective method for spectral unmixing. However, such methods fail to fully exploit the potential of topic models in uncovering image semantics, and they need extra sparsity constraints, which greatly increases the complexity of the model. To solve these problems, a sparse topic relaxion and group clustering model for HU (STRGC) is proposed. In STRGC, the sparse prior constraints implied by the sparse topic model are introduced, which means that the sparse characteristics of the model are used to capture the semantic representation of the spectrum. Through the relaxation of the model, the possible spectral representations of ground features can be obtained, and this further alleviates the influence caused by endmember variability on the accuracy of the unmixing process. Then, fuzzy clustering is used to locate the position of the endmember quickly and accurately. Furthermore, unmixing models with different characteristics are united to alleviate the ill-posed nature of the model, thereby improving the fractional abundance. Experimental results obtained with one simulated dataset and three well-known real hyperspectral datasets confirm the effectiveness and advantages of the proposed method.
abstract_unstemmed	Hyperspectral unmixing (HU) has been a hot research topic in the field of hyperspectral remote sensing. In recent years, the employment of the probabilistic topic model to acquire the latent topics of hyperspectral images has been an effective method for spectral unmixing. However, such methods fail to fully exploit the potential of topic models in uncovering image semantics, and they need extra sparsity constraints, which greatly increases the complexity of the model. To solve these problems, a sparse topic relaxion and group clustering model for HU (STRGC) is proposed. In STRGC, the sparse prior constraints implied by the sparse topic model are introduced, which means that the sparse characteristics of the model are used to capture the semantic representation of the spectrum. Through the relaxation of the model, the possible spectral representations of ground features can be obtained, and this further alleviates the influence caused by endmember variability on the accuracy of the unmixing process. Then, fuzzy clustering is used to locate the position of the endmember quickly and accurately. Furthermore, unmixing models with different characteristics are united to alleviate the ill-posed nature of the model, thereby improving the fractional abundance. Experimental results obtained with one simulated dataset and three well-known real hyperspectral datasets confirm the effectiveness and advantages of the proposed method.
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title_short	A Sparse Topic Relaxion and Group Clustering Model for Hyperspectral Unmixing
url	https://doi.org/10.1109/JSTARS.2021.3069574 https://doaj.org/article/d944df572b614ed0886667a92d00ef64 https://ieeexplore.ieee.org/document/9390184/ https://doaj.org/toc/2151-1535
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Then, fuzzy clustering is used to locate the position of the endmember quickly and accurately. Furthermore, unmixing models with different characteristics are united to alleviate the ill-posed nature of the model, thereby improving the fractional abundance. 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Cosmic physics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Linlin Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Wen Zeng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Qingfeng Guan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhen Hu</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">14(2021), Seite 4014-4027</subfield><subfield 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A Sparse Topic Relaxion and Group Clustering Model for Hyperspectral Unmixing

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