Matrix Autoregressive Model for Hyperspectral Anomaly Detection

For anomaly detection in hyperspectral imagery, the scene can be treated as a combination of the background and the anomalies. Once a pure background hyperspectral image (HSI) is obtained, the anomalies can be easily located. In this article, we detect the anomalies via a matrix autoregressive model...
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Autor*in:	Jingxuan Wang [verfasserIn] Jinqiu Sun [verfasserIn] Yu Zhu [verfasserIn] Yong Xia [verfasserIn] Yanning Zhang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Anomaly detection hyperspectral image (HSI) matrix autoregressive

Übergeordnetes Werk:	In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing - IEEE, 2020, 15(2022), Seite 8656-8667
Übergeordnetes Werk:	volume:15 ; year:2022 ; pages:8656-8667

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/JSTARS.2022.3209204

Katalog-ID:	DOAJ029407419

Internformat


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520			\|a For anomaly detection in hyperspectral imagery, the scene can be treated as a combination of the background and the anomalies. Once a pure background hyperspectral image (HSI) is obtained, the anomalies can be easily located. In this article, we detect the anomalies via a matrix autoregressive model (MARM) to reconstruct the background HSI. Specifically, some informative and discriminative bands are first selected and come into a new HSI with less bands. Second, the new HSI can be treated as a collection of profiles in the row direction. Based on this, the background can be regularly reconstructed via the MARM. The regressive model not only respects the original matrix structure in the row profiles but also utilizes both the spatial and spectral correlations for the detection process. Finally, the classical Reed Xiaoli detector is applied to the difference cube between the band-selected HSI and the HSI reconstructed by MARM, achieving a final detection map with higher accuracy. Experimental results and data analysis on four different sensors captured datasets with different resolutions have validated the effectiveness of the proposed method.
650		4	\|a Anomaly detection
650		4	\|a hyperspectral image (HSI)
650		4	\|a matrix autoregressive
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700	0		\|a Yanning Zhang \|e verfasserin \|4 aut
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allfields	10.1109/JSTARS.2022.3209204 doi (DE-627)DOAJ029407419 (DE-599)DOAJc3d6ad7323134f129aa9a4ceae6b0dce DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Jingxuan Wang verfasserin aut Matrix Autoregressive Model for Hyperspectral Anomaly Detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier For anomaly detection in hyperspectral imagery, the scene can be treated as a combination of the background and the anomalies. Once a pure background hyperspectral image (HSI) is obtained, the anomalies can be easily located. In this article, we detect the anomalies via a matrix autoregressive model (MARM) to reconstruct the background HSI. Specifically, some informative and discriminative bands are first selected and come into a new HSI with less bands. Second, the new HSI can be treated as a collection of profiles in the row direction. Based on this, the background can be regularly reconstructed via the MARM. The regressive model not only respects the original matrix structure in the row profiles but also utilizes both the spatial and spectral correlations for the detection process. Finally, the classical Reed Xiaoli detector is applied to the difference cube between the band-selected HSI and the HSI reconstructed by MARM, achieving a final detection map with higher accuracy. Experimental results and data analysis on four different sensors captured datasets with different resolutions have validated the effectiveness of the proposed method. Anomaly detection hyperspectral image (HSI) matrix autoregressive Ocean engineering Geophysics. Cosmic physics Jinqiu Sun verfasserin aut Yu Zhu verfasserin aut Yong Xia verfasserin aut Yanning Zhang verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 15(2022), Seite 8656-8667 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:15 year:2022 pages:8656-8667 https://doi.org/10.1109/JSTARS.2022.3209204 kostenfrei https://doaj.org/article/c3d6ad7323134f129aa9a4ceae6b0dce kostenfrei https://ieeexplore.ieee.org/document/9903317/ 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 15 2022 8656-8667
spelling	10.1109/JSTARS.2022.3209204 doi (DE-627)DOAJ029407419 (DE-599)DOAJc3d6ad7323134f129aa9a4ceae6b0dce DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Jingxuan Wang verfasserin aut Matrix Autoregressive Model for Hyperspectral Anomaly Detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier For anomaly detection in hyperspectral imagery, the scene can be treated as a combination of the background and the anomalies. Once a pure background hyperspectral image (HSI) is obtained, the anomalies can be easily located. In this article, we detect the anomalies via a matrix autoregressive model (MARM) to reconstruct the background HSI. Specifically, some informative and discriminative bands are first selected and come into a new HSI with less bands. Second, the new HSI can be treated as a collection of profiles in the row direction. Based on this, the background can be regularly reconstructed via the MARM. The regressive model not only respects the original matrix structure in the row profiles but also utilizes both the spatial and spectral correlations for the detection process. Finally, the classical Reed Xiaoli detector is applied to the difference cube between the band-selected HSI and the HSI reconstructed by MARM, achieving a final detection map with higher accuracy. Experimental results and data analysis on four different sensors captured datasets with different resolutions have validated the effectiveness of the proposed method. Anomaly detection hyperspectral image (HSI) matrix autoregressive Ocean engineering Geophysics. Cosmic physics Jinqiu Sun verfasserin aut Yu Zhu verfasserin aut Yong Xia verfasserin aut Yanning Zhang verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 15(2022), Seite 8656-8667 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:15 year:2022 pages:8656-8667 https://doi.org/10.1109/JSTARS.2022.3209204 kostenfrei https://doaj.org/article/c3d6ad7323134f129aa9a4ceae6b0dce kostenfrei https://ieeexplore.ieee.org/document/9903317/ 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 15 2022 8656-8667
allfields_unstemmed	10.1109/JSTARS.2022.3209204 doi (DE-627)DOAJ029407419 (DE-599)DOAJc3d6ad7323134f129aa9a4ceae6b0dce DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Jingxuan Wang verfasserin aut Matrix Autoregressive Model for Hyperspectral Anomaly Detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier For anomaly detection in hyperspectral imagery, the scene can be treated as a combination of the background and the anomalies. Once a pure background hyperspectral image (HSI) is obtained, the anomalies can be easily located. In this article, we detect the anomalies via a matrix autoregressive model (MARM) to reconstruct the background HSI. Specifically, some informative and discriminative bands are first selected and come into a new HSI with less bands. Second, the new HSI can be treated as a collection of profiles in the row direction. Based on this, the background can be regularly reconstructed via the MARM. The regressive model not only respects the original matrix structure in the row profiles but also utilizes both the spatial and spectral correlations for the detection process. Finally, the classical Reed Xiaoli detector is applied to the difference cube between the band-selected HSI and the HSI reconstructed by MARM, achieving a final detection map with higher accuracy. Experimental results and data analysis on four different sensors captured datasets with different resolutions have validated the effectiveness of the proposed method. Anomaly detection hyperspectral image (HSI) matrix autoregressive Ocean engineering Geophysics. Cosmic physics Jinqiu Sun verfasserin aut Yu Zhu verfasserin aut Yong Xia verfasserin aut Yanning Zhang verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 15(2022), Seite 8656-8667 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:15 year:2022 pages:8656-8667 https://doi.org/10.1109/JSTARS.2022.3209204 kostenfrei https://doaj.org/article/c3d6ad7323134f129aa9a4ceae6b0dce kostenfrei https://ieeexplore.ieee.org/document/9903317/ 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 15 2022 8656-8667
allfieldsGer	10.1109/JSTARS.2022.3209204 doi (DE-627)DOAJ029407419 (DE-599)DOAJc3d6ad7323134f129aa9a4ceae6b0dce DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Jingxuan Wang verfasserin aut Matrix Autoregressive Model for Hyperspectral Anomaly Detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier For anomaly detection in hyperspectral imagery, the scene can be treated as a combination of the background and the anomalies. Once a pure background hyperspectral image (HSI) is obtained, the anomalies can be easily located. In this article, we detect the anomalies via a matrix autoregressive model (MARM) to reconstruct the background HSI. Specifically, some informative and discriminative bands are first selected and come into a new HSI with less bands. Second, the new HSI can be treated as a collection of profiles in the row direction. Based on this, the background can be regularly reconstructed via the MARM. The regressive model not only respects the original matrix structure in the row profiles but also utilizes both the spatial and spectral correlations for the detection process. Finally, the classical Reed Xiaoli detector is applied to the difference cube between the band-selected HSI and the HSI reconstructed by MARM, achieving a final detection map with higher accuracy. Experimental results and data analysis on four different sensors captured datasets with different resolutions have validated the effectiveness of the proposed method. Anomaly detection hyperspectral image (HSI) matrix autoregressive Ocean engineering Geophysics. Cosmic physics Jinqiu Sun verfasserin aut Yu Zhu verfasserin aut Yong Xia verfasserin aut Yanning Zhang verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 15(2022), Seite 8656-8667 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:15 year:2022 pages:8656-8667 https://doi.org/10.1109/JSTARS.2022.3209204 kostenfrei https://doaj.org/article/c3d6ad7323134f129aa9a4ceae6b0dce kostenfrei https://ieeexplore.ieee.org/document/9903317/ 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 15 2022 8656-8667
allfieldsSound	10.1109/JSTARS.2022.3209204 doi (DE-627)DOAJ029407419 (DE-599)DOAJc3d6ad7323134f129aa9a4ceae6b0dce DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Jingxuan Wang verfasserin aut Matrix Autoregressive Model for Hyperspectral Anomaly Detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier For anomaly detection in hyperspectral imagery, the scene can be treated as a combination of the background and the anomalies. Once a pure background hyperspectral image (HSI) is obtained, the anomalies can be easily located. In this article, we detect the anomalies via a matrix autoregressive model (MARM) to reconstruct the background HSI. Specifically, some informative and discriminative bands are first selected and come into a new HSI with less bands. Second, the new HSI can be treated as a collection of profiles in the row direction. Based on this, the background can be regularly reconstructed via the MARM. The regressive model not only respects the original matrix structure in the row profiles but also utilizes both the spatial and spectral correlations for the detection process. Finally, the classical Reed Xiaoli detector is applied to the difference cube between the band-selected HSI and the HSI reconstructed by MARM, achieving a final detection map with higher accuracy. Experimental results and data analysis on four different sensors captured datasets with different resolutions have validated the effectiveness of the proposed method. Anomaly detection hyperspectral image (HSI) matrix autoregressive Ocean engineering Geophysics. Cosmic physics Jinqiu Sun verfasserin aut Yu Zhu verfasserin aut Yong Xia verfasserin aut Yanning Zhang verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 15(2022), Seite 8656-8667 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:15 year:2022 pages:8656-8667 https://doi.org/10.1109/JSTARS.2022.3209204 kostenfrei https://doaj.org/article/c3d6ad7323134f129aa9a4ceae6b0dce kostenfrei https://ieeexplore.ieee.org/document/9903317/ 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 15 2022 8656-8667
language	English
source	In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15(2022), Seite 8656-8667 volume:15 year:2022 pages:8656-8667
sourceStr	In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15(2022), Seite 8656-8667 volume:15 year:2022 pages:8656-8667
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Anomaly detection hyperspectral image (HSI) matrix autoregressive 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	Jingxuan Wang @@aut@@ Jinqiu Sun @@aut@@ Yu Zhu @@aut@@ Yong Xia @@aut@@ Yanning Zhang @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	581732634
id	DOAJ029407419
language_de	englisch
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callnumber-first	T - Technology
author	Jingxuan Wang
spellingShingle	Jingxuan Wang misc TC1501-1800 misc QC801-809 misc Anomaly detection misc hyperspectral image (HSI) misc matrix autoregressive misc Ocean engineering misc Geophysics. Cosmic physics Matrix Autoregressive Model for Hyperspectral Anomaly Detection
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topic_title	TC1501-1800 QC801-809 Matrix Autoregressive Model for Hyperspectral Anomaly Detection Anomaly detection hyperspectral image (HSI) matrix autoregressive
topic	misc TC1501-1800 misc QC801-809 misc Anomaly detection misc hyperspectral image (HSI) misc matrix autoregressive misc Ocean engineering misc Geophysics. Cosmic physics
topic_unstemmed	misc TC1501-1800 misc QC801-809 misc Anomaly detection misc hyperspectral image (HSI) misc matrix autoregressive misc Ocean engineering misc Geophysics. Cosmic physics
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title	Matrix Autoregressive Model for Hyperspectral Anomaly Detection
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title_full	Matrix Autoregressive Model for Hyperspectral Anomaly Detection
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title_sort	matrix autoregressive model for hyperspectral anomaly detection
callnumber	TC1501-1800
title_auth	Matrix Autoregressive Model for Hyperspectral Anomaly Detection
abstract	For anomaly detection in hyperspectral imagery, the scene can be treated as a combination of the background and the anomalies. Once a pure background hyperspectral image (HSI) is obtained, the anomalies can be easily located. In this article, we detect the anomalies via a matrix autoregressive model (MARM) to reconstruct the background HSI. Specifically, some informative and discriminative bands are first selected and come into a new HSI with less bands. Second, the new HSI can be treated as a collection of profiles in the row direction. Based on this, the background can be regularly reconstructed via the MARM. The regressive model not only respects the original matrix structure in the row profiles but also utilizes both the spatial and spectral correlations for the detection process. Finally, the classical Reed Xiaoli detector is applied to the difference cube between the band-selected HSI and the HSI reconstructed by MARM, achieving a final detection map with higher accuracy. Experimental results and data analysis on four different sensors captured datasets with different resolutions have validated the effectiveness of the proposed method.
abstractGer	For anomaly detection in hyperspectral imagery, the scene can be treated as a combination of the background and the anomalies. Once a pure background hyperspectral image (HSI) is obtained, the anomalies can be easily located. In this article, we detect the anomalies via a matrix autoregressive model (MARM) to reconstruct the background HSI. Specifically, some informative and discriminative bands are first selected and come into a new HSI with less bands. Second, the new HSI can be treated as a collection of profiles in the row direction. Based on this, the background can be regularly reconstructed via the MARM. The regressive model not only respects the original matrix structure in the row profiles but also utilizes both the spatial and spectral correlations for the detection process. Finally, the classical Reed Xiaoli detector is applied to the difference cube between the band-selected HSI and the HSI reconstructed by MARM, achieving a final detection map with higher accuracy. Experimental results and data analysis on four different sensors captured datasets with different resolutions have validated the effectiveness of the proposed method.
abstract_unstemmed	For anomaly detection in hyperspectral imagery, the scene can be treated as a combination of the background and the anomalies. Once a pure background hyperspectral image (HSI) is obtained, the anomalies can be easily located. In this article, we detect the anomalies via a matrix autoregressive model (MARM) to reconstruct the background HSI. Specifically, some informative and discriminative bands are first selected and come into a new HSI with less bands. Second, the new HSI can be treated as a collection of profiles in the row direction. Based on this, the background can be regularly reconstructed via the MARM. The regressive model not only respects the original matrix structure in the row profiles but also utilizes both the spatial and spectral correlations for the detection process. Finally, the classical Reed Xiaoli detector is applied to the difference cube between the band-selected HSI and the HSI reconstructed by MARM, achieving a final detection map with higher accuracy. Experimental results and data analysis on four different sensors captured datasets with different resolutions have validated the effectiveness of the proposed method.
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title_short	Matrix Autoregressive Model for Hyperspectral Anomaly Detection
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score	7.40022

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Matrix Autoregressive Model for Hyperspectral Anomaly Detection

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