Hyperspectral image classification via active learning and broad learning system
Abstract Hyperspectral image (HSI) classification has continued to be a hot research topic in recent years, and the broad learning system (BLS) has been considered by scholars for the classification of HSIs due to its superior internal structure. Different from the traditional HSI classification mec...
Ausführliche Beschreibung
Autor*in: |
Huang, Huifang [verfasserIn] |
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Englisch |
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2022 |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 53(2022), 12 vom: 25. Nov., Seite 15683-15694 |
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Übergeordnetes Werk: |
volume:53 ; year:2022 ; number:12 ; day:25 ; month:11 ; pages:15683-15694 |
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DOI / URN: |
10.1007/s10489-021-02805-5 |
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OLC2143604793 |
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520 | |a Abstract Hyperspectral image (HSI) classification has continued to be a hot research topic in recent years, and the broad learning system (BLS) has been considered by scholars for the classification of HSIs due to its superior internal structure. Different from the traditional HSI classification mechanism, this paper proposes an active broad learning system approach for HSI classification. The spectral and spatial features of the image are extracted using principal component analysis and local binary patterns, respectively. Then, the vector fusion of the above two features is utilized as the input of the BLS and trained to obtain pre-labels of the samples. The next training samples are selected among the pre-labels by active learning. Unlike other classification algorithms, the method proposed in this paper utilizes active learning (AL) to select high-quality samples for training, thereby reducing the number of samples used and the cost of sample labeling. In addition, the use of incremental learning in broad learning significantly reduces the training time and improves the classification accuracy. The algorithm proposed in this paper is more effective compared to other state-of-the-art algorithms on three HSI datasets. | ||
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700 | 1 | |a Chen, C. L. Philip |4 aut | |
700 | 1 | |a Zhang, Yun |4 aut | |
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10.1007/s10489-021-02805-5 doi (DE-627)OLC2143604793 (DE-He213)s10489-021-02805-5-p DE-627 ger DE-627 rakwb eng 004 VZ Huang, Huifang verfasserin aut Hyperspectral image classification via active learning and broad learning system 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Hyperspectral image (HSI) classification has continued to be a hot research topic in recent years, and the broad learning system (BLS) has been considered by scholars for the classification of HSIs due to its superior internal structure. Different from the traditional HSI classification mechanism, this paper proposes an active broad learning system approach for HSI classification. The spectral and spatial features of the image are extracted using principal component analysis and local binary patterns, respectively. Then, the vector fusion of the above two features is utilized as the input of the BLS and trained to obtain pre-labels of the samples. The next training samples are selected among the pre-labels by active learning. Unlike other classification algorithms, the method proposed in this paper utilizes active learning (AL) to select high-quality samples for training, thereby reducing the number of samples used and the cost of sample labeling. In addition, the use of incremental learning in broad learning significantly reduces the training time and improves the classification accuracy. The algorithm proposed in this paper is more effective compared to other state-of-the-art algorithms on three HSI datasets. Hyperspectral image Active learning Broad learning system Classification Liu, Zhi aut Chen, C. L. Philip aut Zhang, Yun aut Enthalten in Applied intelligence Springer US, 1991 53(2022), 12 vom: 25. Nov., Seite 15683-15694 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:53 year:2022 number:12 day:25 month:11 pages:15683-15694 https://doi.org/10.1007/s10489-021-02805-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 53 2022 12 25 11 15683-15694 |
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10.1007/s10489-021-02805-5 doi (DE-627)OLC2143604793 (DE-He213)s10489-021-02805-5-p DE-627 ger DE-627 rakwb eng 004 VZ Huang, Huifang verfasserin aut Hyperspectral image classification via active learning and broad learning system 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Hyperspectral image (HSI) classification has continued to be a hot research topic in recent years, and the broad learning system (BLS) has been considered by scholars for the classification of HSIs due to its superior internal structure. Different from the traditional HSI classification mechanism, this paper proposes an active broad learning system approach for HSI classification. The spectral and spatial features of the image are extracted using principal component analysis and local binary patterns, respectively. Then, the vector fusion of the above two features is utilized as the input of the BLS and trained to obtain pre-labels of the samples. The next training samples are selected among the pre-labels by active learning. Unlike other classification algorithms, the method proposed in this paper utilizes active learning (AL) to select high-quality samples for training, thereby reducing the number of samples used and the cost of sample labeling. In addition, the use of incremental learning in broad learning significantly reduces the training time and improves the classification accuracy. The algorithm proposed in this paper is more effective compared to other state-of-the-art algorithms on three HSI datasets. Hyperspectral image Active learning Broad learning system Classification Liu, Zhi aut Chen, C. L. Philip aut Zhang, Yun aut Enthalten in Applied intelligence Springer US, 1991 53(2022), 12 vom: 25. Nov., Seite 15683-15694 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:53 year:2022 number:12 day:25 month:11 pages:15683-15694 https://doi.org/10.1007/s10489-021-02805-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 53 2022 12 25 11 15683-15694 |
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10.1007/s10489-021-02805-5 doi (DE-627)OLC2143604793 (DE-He213)s10489-021-02805-5-p DE-627 ger DE-627 rakwb eng 004 VZ Huang, Huifang verfasserin aut Hyperspectral image classification via active learning and broad learning system 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Hyperspectral image (HSI) classification has continued to be a hot research topic in recent years, and the broad learning system (BLS) has been considered by scholars for the classification of HSIs due to its superior internal structure. Different from the traditional HSI classification mechanism, this paper proposes an active broad learning system approach for HSI classification. The spectral and spatial features of the image are extracted using principal component analysis and local binary patterns, respectively. Then, the vector fusion of the above two features is utilized as the input of the BLS and trained to obtain pre-labels of the samples. The next training samples are selected among the pre-labels by active learning. Unlike other classification algorithms, the method proposed in this paper utilizes active learning (AL) to select high-quality samples for training, thereby reducing the number of samples used and the cost of sample labeling. In addition, the use of incremental learning in broad learning significantly reduces the training time and improves the classification accuracy. The algorithm proposed in this paper is more effective compared to other state-of-the-art algorithms on three HSI datasets. Hyperspectral image Active learning Broad learning system Classification Liu, Zhi aut Chen, C. L. Philip aut Zhang, Yun aut Enthalten in Applied intelligence Springer US, 1991 53(2022), 12 vom: 25. Nov., Seite 15683-15694 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:53 year:2022 number:12 day:25 month:11 pages:15683-15694 https://doi.org/10.1007/s10489-021-02805-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 53 2022 12 25 11 15683-15694 |
allfieldsGer |
10.1007/s10489-021-02805-5 doi (DE-627)OLC2143604793 (DE-He213)s10489-021-02805-5-p DE-627 ger DE-627 rakwb eng 004 VZ Huang, Huifang verfasserin aut Hyperspectral image classification via active learning and broad learning system 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Hyperspectral image (HSI) classification has continued to be a hot research topic in recent years, and the broad learning system (BLS) has been considered by scholars for the classification of HSIs due to its superior internal structure. Different from the traditional HSI classification mechanism, this paper proposes an active broad learning system approach for HSI classification. The spectral and spatial features of the image are extracted using principal component analysis and local binary patterns, respectively. Then, the vector fusion of the above two features is utilized as the input of the BLS and trained to obtain pre-labels of the samples. The next training samples are selected among the pre-labels by active learning. Unlike other classification algorithms, the method proposed in this paper utilizes active learning (AL) to select high-quality samples for training, thereby reducing the number of samples used and the cost of sample labeling. In addition, the use of incremental learning in broad learning significantly reduces the training time and improves the classification accuracy. The algorithm proposed in this paper is more effective compared to other state-of-the-art algorithms on three HSI datasets. Hyperspectral image Active learning Broad learning system Classification Liu, Zhi aut Chen, C. L. Philip aut Zhang, Yun aut Enthalten in Applied intelligence Springer US, 1991 53(2022), 12 vom: 25. Nov., Seite 15683-15694 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:53 year:2022 number:12 day:25 month:11 pages:15683-15694 https://doi.org/10.1007/s10489-021-02805-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 53 2022 12 25 11 15683-15694 |
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10.1007/s10489-021-02805-5 doi (DE-627)OLC2143604793 (DE-He213)s10489-021-02805-5-p DE-627 ger DE-627 rakwb eng 004 VZ Huang, Huifang verfasserin aut Hyperspectral image classification via active learning and broad learning system 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Hyperspectral image (HSI) classification has continued to be a hot research topic in recent years, and the broad learning system (BLS) has been considered by scholars for the classification of HSIs due to its superior internal structure. Different from the traditional HSI classification mechanism, this paper proposes an active broad learning system approach for HSI classification. The spectral and spatial features of the image are extracted using principal component analysis and local binary patterns, respectively. Then, the vector fusion of the above two features is utilized as the input of the BLS and trained to obtain pre-labels of the samples. The next training samples are selected among the pre-labels by active learning. Unlike other classification algorithms, the method proposed in this paper utilizes active learning (AL) to select high-quality samples for training, thereby reducing the number of samples used and the cost of sample labeling. In addition, the use of incremental learning in broad learning significantly reduces the training time and improves the classification accuracy. The algorithm proposed in this paper is more effective compared to other state-of-the-art algorithms on three HSI datasets. Hyperspectral image Active learning Broad learning system Classification Liu, Zhi aut Chen, C. L. Philip aut Zhang, Yun aut Enthalten in Applied intelligence Springer US, 1991 53(2022), 12 vom: 25. Nov., Seite 15683-15694 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:53 year:2022 number:12 day:25 month:11 pages:15683-15694 https://doi.org/10.1007/s10489-021-02805-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 53 2022 12 25 11 15683-15694 |
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Abstract Hyperspectral image (HSI) classification has continued to be a hot research topic in recent years, and the broad learning system (BLS) has been considered by scholars for the classification of HSIs due to its superior internal structure. Different from the traditional HSI classification mechanism, this paper proposes an active broad learning system approach for HSI classification. The spectral and spatial features of the image are extracted using principal component analysis and local binary patterns, respectively. Then, the vector fusion of the above two features is utilized as the input of the BLS and trained to obtain pre-labels of the samples. The next training samples are selected among the pre-labels by active learning. Unlike other classification algorithms, the method proposed in this paper utilizes active learning (AL) to select high-quality samples for training, thereby reducing the number of samples used and the cost of sample labeling. In addition, the use of incremental learning in broad learning significantly reduces the training time and improves the classification accuracy. The algorithm proposed in this paper is more effective compared to other state-of-the-art algorithms on three HSI datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstractGer |
Abstract Hyperspectral image (HSI) classification has continued to be a hot research topic in recent years, and the broad learning system (BLS) has been considered by scholars for the classification of HSIs due to its superior internal structure. Different from the traditional HSI classification mechanism, this paper proposes an active broad learning system approach for HSI classification. The spectral and spatial features of the image are extracted using principal component analysis and local binary patterns, respectively. Then, the vector fusion of the above two features is utilized as the input of the BLS and trained to obtain pre-labels of the samples. The next training samples are selected among the pre-labels by active learning. Unlike other classification algorithms, the method proposed in this paper utilizes active learning (AL) to select high-quality samples for training, thereby reducing the number of samples used and the cost of sample labeling. In addition, the use of incremental learning in broad learning significantly reduces the training time and improves the classification accuracy. The algorithm proposed in this paper is more effective compared to other state-of-the-art algorithms on three HSI datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstract_unstemmed |
Abstract Hyperspectral image (HSI) classification has continued to be a hot research topic in recent years, and the broad learning system (BLS) has been considered by scholars for the classification of HSIs due to its superior internal structure. Different from the traditional HSI classification mechanism, this paper proposes an active broad learning system approach for HSI classification. The spectral and spatial features of the image are extracted using principal component analysis and local binary patterns, respectively. Then, the vector fusion of the above two features is utilized as the input of the BLS and trained to obtain pre-labels of the samples. The next training samples are selected among the pre-labels by active learning. Unlike other classification algorithms, the method proposed in this paper utilizes active learning (AL) to select high-quality samples for training, thereby reducing the number of samples used and the cost of sample labeling. In addition, the use of incremental learning in broad learning significantly reduces the training time and improves the classification accuracy. The algorithm proposed in this paper is more effective compared to other state-of-the-art algorithms on three HSI datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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title_short |
Hyperspectral image classification via active learning and broad learning system |
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https://doi.org/10.1007/s10489-021-02805-5 |
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Liu, Zhi Chen, C. L. Philip Zhang, Yun |
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