Structure Extraction With Total Variation for Hyperspectral Image Classification

This paper proposes a novel structure extraction approach that is able to achieve high classification accuracy and low computing burden to hyperspectral image (HSI) classification based on total variation (SETV). Specifically, a two-scale decomposition-based relative total variation (TSD-RTV) method...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Qiaoqiao Li [verfasserIn] Haibo Wang [verfasserIn] Guoyue Chen [verfasserIn] Kazuki Saruta [verfasserIn] Yuki Terata [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Structure extraction hyperspectral image classification total variation fusion

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 91019-91033
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:91019-91033

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2922675

Katalog-ID:	DOAJ007349645

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allfields	10.1109/ACCESS.2019.2922675 doi (DE-627)DOAJ007349645 (DE-599)DOAJe790303579c642ddaf6f2d4cec862424 DE-627 ger DE-627 rakwb eng TK1-9971 Qiaoqiao Li verfasserin aut Structure Extraction With Total Variation for Hyperspectral Image Classification 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a novel structure extraction approach that is able to achieve high classification accuracy and low computing burden to hyperspectral image (HSI) classification based on total variation (SETV). Specifically, a two-scale decomposition-based relative total variation (TSD-RTV) method is presented for the first time to process the information of different scales, such that the structure can be well extracted. Moreover, a new weighted-average fusion method is introduced, which can reduce the dimensionality and also remove the noise due to hyperspectral sensors. The support vector machine (SVM) is applied to HSI classification as a classifier. The experiments are conducted on three real hyperspectral datasets: Indian Pines, Salinas, and Kennedy Space Center. The experimental results show the outstanding performance of the proposed SETV model in terms of classification accuracy and computational efficiency. Structure extraction hyperspectral image classification total variation fusion Electrical engineering. Electronics. Nuclear engineering Haibo Wang verfasserin aut Guoyue Chen verfasserin aut Kazuki Saruta verfasserin aut Yuki Terata verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 91019-91033 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:91019-91033 https://doi.org/10.1109/ACCESS.2019.2922675 kostenfrei https://doaj.org/article/e790303579c642ddaf6f2d4cec862424 kostenfrei https://ieeexplore.ieee.org/document/8736233/ kostenfrei https://doaj.org/toc/2169-3536 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4700 AR 7 2019 91019-91033
spelling	10.1109/ACCESS.2019.2922675 doi (DE-627)DOAJ007349645 (DE-599)DOAJe790303579c642ddaf6f2d4cec862424 DE-627 ger DE-627 rakwb eng TK1-9971 Qiaoqiao Li verfasserin aut Structure Extraction With Total Variation for Hyperspectral Image Classification 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a novel structure extraction approach that is able to achieve high classification accuracy and low computing burden to hyperspectral image (HSI) classification based on total variation (SETV). Specifically, a two-scale decomposition-based relative total variation (TSD-RTV) method is presented for the first time to process the information of different scales, such that the structure can be well extracted. Moreover, a new weighted-average fusion method is introduced, which can reduce the dimensionality and also remove the noise due to hyperspectral sensors. The support vector machine (SVM) is applied to HSI classification as a classifier. The experiments are conducted on three real hyperspectral datasets: Indian Pines, Salinas, and Kennedy Space Center. The experimental results show the outstanding performance of the proposed SETV model in terms of classification accuracy and computational efficiency. Structure extraction hyperspectral image classification total variation fusion Electrical engineering. Electronics. Nuclear engineering Haibo Wang verfasserin aut Guoyue Chen verfasserin aut Kazuki Saruta verfasserin aut Yuki Terata verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 91019-91033 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:91019-91033 https://doi.org/10.1109/ACCESS.2019.2922675 kostenfrei https://doaj.org/article/e790303579c642ddaf6f2d4cec862424 kostenfrei https://ieeexplore.ieee.org/document/8736233/ kostenfrei https://doaj.org/toc/2169-3536 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4700 AR 7 2019 91019-91033
allfields_unstemmed	10.1109/ACCESS.2019.2922675 doi (DE-627)DOAJ007349645 (DE-599)DOAJe790303579c642ddaf6f2d4cec862424 DE-627 ger DE-627 rakwb eng TK1-9971 Qiaoqiao Li verfasserin aut Structure Extraction With Total Variation for Hyperspectral Image Classification 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a novel structure extraction approach that is able to achieve high classification accuracy and low computing burden to hyperspectral image (HSI) classification based on total variation (SETV). Specifically, a two-scale decomposition-based relative total variation (TSD-RTV) method is presented for the first time to process the information of different scales, such that the structure can be well extracted. Moreover, a new weighted-average fusion method is introduced, which can reduce the dimensionality and also remove the noise due to hyperspectral sensors. The support vector machine (SVM) is applied to HSI classification as a classifier. The experiments are conducted on three real hyperspectral datasets: Indian Pines, Salinas, and Kennedy Space Center. The experimental results show the outstanding performance of the proposed SETV model in terms of classification accuracy and computational efficiency. Structure extraction hyperspectral image classification total variation fusion Electrical engineering. Electronics. Nuclear engineering Haibo Wang verfasserin aut Guoyue Chen verfasserin aut Kazuki Saruta verfasserin aut Yuki Terata verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 91019-91033 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:91019-91033 https://doi.org/10.1109/ACCESS.2019.2922675 kostenfrei https://doaj.org/article/e790303579c642ddaf6f2d4cec862424 kostenfrei https://ieeexplore.ieee.org/document/8736233/ kostenfrei https://doaj.org/toc/2169-3536 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4700 AR 7 2019 91019-91033
allfieldsGer	10.1109/ACCESS.2019.2922675 doi (DE-627)DOAJ007349645 (DE-599)DOAJe790303579c642ddaf6f2d4cec862424 DE-627 ger DE-627 rakwb eng TK1-9971 Qiaoqiao Li verfasserin aut Structure Extraction With Total Variation for Hyperspectral Image Classification 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a novel structure extraction approach that is able to achieve high classification accuracy and low computing burden to hyperspectral image (HSI) classification based on total variation (SETV). Specifically, a two-scale decomposition-based relative total variation (TSD-RTV) method is presented for the first time to process the information of different scales, such that the structure can be well extracted. Moreover, a new weighted-average fusion method is introduced, which can reduce the dimensionality and also remove the noise due to hyperspectral sensors. The support vector machine (SVM) is applied to HSI classification as a classifier. The experiments are conducted on three real hyperspectral datasets: Indian Pines, Salinas, and Kennedy Space Center. The experimental results show the outstanding performance of the proposed SETV model in terms of classification accuracy and computational efficiency. Structure extraction hyperspectral image classification total variation fusion Electrical engineering. Electronics. Nuclear engineering Haibo Wang verfasserin aut Guoyue Chen verfasserin aut Kazuki Saruta verfasserin aut Yuki Terata verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 91019-91033 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:91019-91033 https://doi.org/10.1109/ACCESS.2019.2922675 kostenfrei https://doaj.org/article/e790303579c642ddaf6f2d4cec862424 kostenfrei https://ieeexplore.ieee.org/document/8736233/ kostenfrei https://doaj.org/toc/2169-3536 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4700 AR 7 2019 91019-91033
allfieldsSound	10.1109/ACCESS.2019.2922675 doi (DE-627)DOAJ007349645 (DE-599)DOAJe790303579c642ddaf6f2d4cec862424 DE-627 ger DE-627 rakwb eng TK1-9971 Qiaoqiao Li verfasserin aut Structure Extraction With Total Variation for Hyperspectral Image Classification 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a novel structure extraction approach that is able to achieve high classification accuracy and low computing burden to hyperspectral image (HSI) classification based on total variation (SETV). Specifically, a two-scale decomposition-based relative total variation (TSD-RTV) method is presented for the first time to process the information of different scales, such that the structure can be well extracted. Moreover, a new weighted-average fusion method is introduced, which can reduce the dimensionality and also remove the noise due to hyperspectral sensors. The support vector machine (SVM) is applied to HSI classification as a classifier. The experiments are conducted on three real hyperspectral datasets: Indian Pines, Salinas, and Kennedy Space Center. The experimental results show the outstanding performance of the proposed SETV model in terms of classification accuracy and computational efficiency. Structure extraction hyperspectral image classification total variation fusion Electrical engineering. Electronics. Nuclear engineering Haibo Wang verfasserin aut Guoyue Chen verfasserin aut Kazuki Saruta verfasserin aut Yuki Terata verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 91019-91033 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:91019-91033 https://doi.org/10.1109/ACCESS.2019.2922675 kostenfrei https://doaj.org/article/e790303579c642ddaf6f2d4cec862424 kostenfrei https://ieeexplore.ieee.org/document/8736233/ kostenfrei https://doaj.org/toc/2169-3536 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4700 AR 7 2019 91019-91033
language	English
source	In IEEE Access 7(2019), Seite 91019-91033 volume:7 year:2019 pages:91019-91033
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topic_facet	Structure extraction hyperspectral image classification total variation fusion Electrical engineering. Electronics. Nuclear engineering
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authorswithroles_txt_mv	Qiaoqiao Li @@aut@@ Haibo Wang @@aut@@ Guoyue Chen @@aut@@ Kazuki Saruta @@aut@@ Yuki Terata @@aut@@
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callnumber-first	T - Technology
author	Qiaoqiao Li
spellingShingle	Qiaoqiao Li misc TK1-9971 misc Structure extraction misc hyperspectral image classification misc total variation misc fusion misc Electrical engineering. Electronics. Nuclear engineering Structure Extraction With Total Variation for Hyperspectral Image Classification
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topic_title	TK1-9971 Structure Extraction With Total Variation for Hyperspectral Image Classification Structure extraction hyperspectral image classification total variation fusion
topic	misc TK1-9971 misc Structure extraction misc hyperspectral image classification misc total variation misc fusion misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Structure extraction misc hyperspectral image classification misc total variation misc fusion misc Electrical engineering. Electronics. Nuclear engineering
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title	Structure Extraction With Total Variation for Hyperspectral Image Classification
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title_sort	structure extraction with total variation for hyperspectral image classification
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title_auth	Structure Extraction With Total Variation for Hyperspectral Image Classification
abstract	This paper proposes a novel structure extraction approach that is able to achieve high classification accuracy and low computing burden to hyperspectral image (HSI) classification based on total variation (SETV). Specifically, a two-scale decomposition-based relative total variation (TSD-RTV) method is presented for the first time to process the information of different scales, such that the structure can be well extracted. Moreover, a new weighted-average fusion method is introduced, which can reduce the dimensionality and also remove the noise due to hyperspectral sensors. The support vector machine (SVM) is applied to HSI classification as a classifier. The experiments are conducted on three real hyperspectral datasets: Indian Pines, Salinas, and Kennedy Space Center. The experimental results show the outstanding performance of the proposed SETV model in terms of classification accuracy and computational efficiency.
abstractGer	This paper proposes a novel structure extraction approach that is able to achieve high classification accuracy and low computing burden to hyperspectral image (HSI) classification based on total variation (SETV). Specifically, a two-scale decomposition-based relative total variation (TSD-RTV) method is presented for the first time to process the information of different scales, such that the structure can be well extracted. Moreover, a new weighted-average fusion method is introduced, which can reduce the dimensionality and also remove the noise due to hyperspectral sensors. The support vector machine (SVM) is applied to HSI classification as a classifier. The experiments are conducted on three real hyperspectral datasets: Indian Pines, Salinas, and Kennedy Space Center. The experimental results show the outstanding performance of the proposed SETV model in terms of classification accuracy and computational efficiency.
abstract_unstemmed	This paper proposes a novel structure extraction approach that is able to achieve high classification accuracy and low computing burden to hyperspectral image (HSI) classification based on total variation (SETV). Specifically, a two-scale decomposition-based relative total variation (TSD-RTV) method is presented for the first time to process the information of different scales, such that the structure can be well extracted. Moreover, a new weighted-average fusion method is introduced, which can reduce the dimensionality and also remove the noise due to hyperspectral sensors. The support vector machine (SVM) is applied to HSI classification as a classifier. The experiments are conducted on three real hyperspectral datasets: Indian Pines, Salinas, and Kennedy Space Center. The experimental results show the outstanding performance of the proposed SETV model in terms of classification accuracy and computational efficiency.
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title_short	Structure Extraction With Total Variation for Hyperspectral Image Classification
url	https://doi.org/10.1109/ACCESS.2019.2922675 https://doaj.org/article/e790303579c642ddaf6f2d4cec862424 https://ieeexplore.ieee.org/document/8736233/ https://doaj.org/toc/2169-3536
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Structure Extraction With Total Variation for Hyperspectral Image Classification

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