Band selection using spectral and spatial information in particle swarm optimization for hyperspectral image classification
Abstract Dimensionality reduction (DR) is an effective preprocessing step in hyperspectral image (HSI) analysis. A particle swarm optimization (PSO)-based unsupervised DR method is proposed in the present paper where spectral divergence as well as spatial gradient information is used in selecting in...
Ausführliche Beschreibung
Autor*in: |
Paul, Arati [verfasserIn] |
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Englisch |
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2022 |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer Berlin Heidelberg, 1997, 26(2022), 6 vom: 12. Feb., Seite 2819-2834 |
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Übergeordnetes Werk: |
volume:26 ; year:2022 ; number:6 ; day:12 ; month:02 ; pages:2819-2834 |
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DOI / URN: |
10.1007/s00500-022-06821-6 |
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OLC2078156329 |
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650 | 4 | |a Band selection | |
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10.1007/s00500-022-06821-6 doi (DE-627)OLC2078156329 (DE-He213)s00500-022-06821-6-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Paul, Arati verfasserin (orcid)0000-0003-0422-5656 aut Band selection using spectral and spatial information in particle swarm optimization for hyperspectral image classification 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Dimensionality reduction (DR) is an effective preprocessing step in hyperspectral image (HSI) analysis. A particle swarm optimization (PSO)-based unsupervised DR method is proposed in the present paper where spectral divergence as well as spatial gradient information is used in selecting informative bands. In general, HSI is negatively affected by low signal-to-noise ratio (SNR). Hence, in the proposed method, a noise filter is applied to minimize the effect of noise in band selection. Clustering is introduced to reduce spatial redundancy and extract distinct patterns from the data. This enables improvement in the computation performance of each iteration in PSO. The proposed method is applied on two standard datasets, and the performance is evaluated using overall classification accuracy. Finally, results are compared with other recent state-of-the-art methods where the proposed method performed reasonably better than other tested methods in terms of consistency and classification accuracy. Band selection Particle swarm optimization Clustering Hyperspectral Unsupervised Chaki, Nabendu aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 26(2022), 6 vom: 12. Feb., Seite 2819-2834 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:26 year:2022 number:6 day:12 month:02 pages:2819-2834 https://doi.org/10.1007/s00500-022-06821-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 26 2022 6 12 02 2819-2834 |
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10.1007/s00500-022-06821-6 doi (DE-627)OLC2078156329 (DE-He213)s00500-022-06821-6-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Paul, Arati verfasserin (orcid)0000-0003-0422-5656 aut Band selection using spectral and spatial information in particle swarm optimization for hyperspectral image classification 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Dimensionality reduction (DR) is an effective preprocessing step in hyperspectral image (HSI) analysis. A particle swarm optimization (PSO)-based unsupervised DR method is proposed in the present paper where spectral divergence as well as spatial gradient information is used in selecting informative bands. In general, HSI is negatively affected by low signal-to-noise ratio (SNR). Hence, in the proposed method, a noise filter is applied to minimize the effect of noise in band selection. Clustering is introduced to reduce spatial redundancy and extract distinct patterns from the data. This enables improvement in the computation performance of each iteration in PSO. The proposed method is applied on two standard datasets, and the performance is evaluated using overall classification accuracy. Finally, results are compared with other recent state-of-the-art methods where the proposed method performed reasonably better than other tested methods in terms of consistency and classification accuracy. Band selection Particle swarm optimization Clustering Hyperspectral Unsupervised Chaki, Nabendu aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 26(2022), 6 vom: 12. Feb., Seite 2819-2834 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:26 year:2022 number:6 day:12 month:02 pages:2819-2834 https://doi.org/10.1007/s00500-022-06821-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 26 2022 6 12 02 2819-2834 |
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10.1007/s00500-022-06821-6 doi (DE-627)OLC2078156329 (DE-He213)s00500-022-06821-6-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Paul, Arati verfasserin (orcid)0000-0003-0422-5656 aut Band selection using spectral and spatial information in particle swarm optimization for hyperspectral image classification 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Dimensionality reduction (DR) is an effective preprocessing step in hyperspectral image (HSI) analysis. A particle swarm optimization (PSO)-based unsupervised DR method is proposed in the present paper where spectral divergence as well as spatial gradient information is used in selecting informative bands. In general, HSI is negatively affected by low signal-to-noise ratio (SNR). Hence, in the proposed method, a noise filter is applied to minimize the effect of noise in band selection. Clustering is introduced to reduce spatial redundancy and extract distinct patterns from the data. This enables improvement in the computation performance of each iteration in PSO. The proposed method is applied on two standard datasets, and the performance is evaluated using overall classification accuracy. Finally, results are compared with other recent state-of-the-art methods where the proposed method performed reasonably better than other tested methods in terms of consistency and classification accuracy. Band selection Particle swarm optimization Clustering Hyperspectral Unsupervised Chaki, Nabendu aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 26(2022), 6 vom: 12. Feb., Seite 2819-2834 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:26 year:2022 number:6 day:12 month:02 pages:2819-2834 https://doi.org/10.1007/s00500-022-06821-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 26 2022 6 12 02 2819-2834 |
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10.1007/s00500-022-06821-6 doi (DE-627)OLC2078156329 (DE-He213)s00500-022-06821-6-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Paul, Arati verfasserin (orcid)0000-0003-0422-5656 aut Band selection using spectral and spatial information in particle swarm optimization for hyperspectral image classification 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Dimensionality reduction (DR) is an effective preprocessing step in hyperspectral image (HSI) analysis. A particle swarm optimization (PSO)-based unsupervised DR method is proposed in the present paper where spectral divergence as well as spatial gradient information is used in selecting informative bands. In general, HSI is negatively affected by low signal-to-noise ratio (SNR). Hence, in the proposed method, a noise filter is applied to minimize the effect of noise in band selection. Clustering is introduced to reduce spatial redundancy and extract distinct patterns from the data. This enables improvement in the computation performance of each iteration in PSO. The proposed method is applied on two standard datasets, and the performance is evaluated using overall classification accuracy. Finally, results are compared with other recent state-of-the-art methods where the proposed method performed reasonably better than other tested methods in terms of consistency and classification accuracy. Band selection Particle swarm optimization Clustering Hyperspectral Unsupervised Chaki, Nabendu aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 26(2022), 6 vom: 12. Feb., Seite 2819-2834 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:26 year:2022 number:6 day:12 month:02 pages:2819-2834 https://doi.org/10.1007/s00500-022-06821-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 26 2022 6 12 02 2819-2834 |
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Abstract Dimensionality reduction (DR) is an effective preprocessing step in hyperspectral image (HSI) analysis. A particle swarm optimization (PSO)-based unsupervised DR method is proposed in the present paper where spectral divergence as well as spatial gradient information is used in selecting informative bands. In general, HSI is negatively affected by low signal-to-noise ratio (SNR). Hence, in the proposed method, a noise filter is applied to minimize the effect of noise in band selection. Clustering is introduced to reduce spatial redundancy and extract distinct patterns from the data. This enables improvement in the computation performance of each iteration in PSO. The proposed method is applied on two standard datasets, and the performance is evaluated using overall classification accuracy. Finally, results are compared with other recent state-of-the-art methods where the proposed method performed reasonably better than other tested methods in terms of consistency and classification accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
abstractGer |
Abstract Dimensionality reduction (DR) is an effective preprocessing step in hyperspectral image (HSI) analysis. A particle swarm optimization (PSO)-based unsupervised DR method is proposed in the present paper where spectral divergence as well as spatial gradient information is used in selecting informative bands. In general, HSI is negatively affected by low signal-to-noise ratio (SNR). Hence, in the proposed method, a noise filter is applied to minimize the effect of noise in band selection. Clustering is introduced to reduce spatial redundancy and extract distinct patterns from the data. This enables improvement in the computation performance of each iteration in PSO. The proposed method is applied on two standard datasets, and the performance is evaluated using overall classification accuracy. Finally, results are compared with other recent state-of-the-art methods where the proposed method performed reasonably better than other tested methods in terms of consistency and classification accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
abstract_unstemmed |
Abstract Dimensionality reduction (DR) is an effective preprocessing step in hyperspectral image (HSI) analysis. A particle swarm optimization (PSO)-based unsupervised DR method is proposed in the present paper where spectral divergence as well as spatial gradient information is used in selecting informative bands. In general, HSI is negatively affected by low signal-to-noise ratio (SNR). Hence, in the proposed method, a noise filter is applied to minimize the effect of noise in band selection. Clustering is introduced to reduce spatial redundancy and extract distinct patterns from the data. This enables improvement in the computation performance of each iteration in PSO. The proposed method is applied on two standard datasets, and the performance is evaluated using overall classification accuracy. Finally, results are compared with other recent state-of-the-art methods where the proposed method performed reasonably better than other tested methods in terms of consistency and classification accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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Band selection using spectral and spatial information in particle swarm optimization for hyperspectral image classification |
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https://doi.org/10.1007/s00500-022-06821-6 |
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Chaki, Nabendu |
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