SSNET: an improved deep hybrid network for hyperspectral image classification
Abstract Classification is one of the most important task in hyperspectral image processing. In the last few decades, several classification techniques have been introduced. However, most of them could not efficiently extract features from hyperspectral images (HSI). A novel deep learning framework...
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| Autor*in: |
Paul, Arati [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
Convolutional neural networks (CNN) |
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| Anmerkung: |
© Springer-Verlag London Ltd., part of Springer Nature 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 33(2020), 5 vom: 16. Juni, Seite 1575-1585 |
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| Übergeordnetes Werk: |
volume:33 ; year:2020 ; number:5 ; day:16 ; month:06 ; pages:1575-1585 |
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| DOI / URN: |
10.1007/s00521-020-05069-1 |
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OLC2124043714 |
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| 520 | |a Abstract Classification is one of the most important task in hyperspectral image processing. In the last few decades, several classification techniques have been introduced. However, most of them could not efficiently extract features from hyperspectral images (HSI). A novel deep learning framework is proposed in this paper which efficiently utilises convolutional neural network (CNN) and spatial pyramid pooling (SPP) for extracting both the spectral–spatial features for classification. The proposed hybrid framework uses principal component analysis (PCA), 3D-CNN, 2D-CNN and SPP. The proposed CNN-based model is applied on three benchmark hyperspectral datasets, and subsequently the performance is compared with state-of-the-art methods in the same field. The obtained results reveal the superiority of the proposed model in effectively classifying HSI. | ||
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10.1007/s00521-020-05069-1 doi (DE-627)OLC2124043714 (DE-He213)s00521-020-05069-1-p DE-627 ger DE-627 rakwb eng 004 VZ Paul, Arati verfasserin aut SSNET: an improved deep hybrid network for hyperspectral image classification 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract Classification is one of the most important task in hyperspectral image processing. In the last few decades, several classification techniques have been introduced. However, most of them could not efficiently extract features from hyperspectral images (HSI). A novel deep learning framework is proposed in this paper which efficiently utilises convolutional neural network (CNN) and spatial pyramid pooling (SPP) for extracting both the spectral–spatial features for classification. The proposed hybrid framework uses principal component analysis (PCA), 3D-CNN, 2D-CNN and SPP. The proposed CNN-based model is applied on three benchmark hyperspectral datasets, and subsequently the performance is compared with state-of-the-art methods in the same field. The obtained results reveal the superiority of the proposed model in effectively classifying HSI. Convolutional neural networks (CNN) Hyperspectral image classification 3D-CNN 2D-CNN Spatial pyramid pooling (SPP) Bhoumik, Sanghamita aut Chaki, Nabendu aut Enthalten in Neural computing & applications Springer London, 1993 33(2020), 5 vom: 16. Juni, Seite 1575-1585 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:33 year:2020 number:5 day:16 month:06 pages:1575-1585 https://doi.org/10.1007/s00521-020-05069-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 33 2020 5 16 06 1575-1585 |
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10.1007/s00521-020-05069-1 doi (DE-627)OLC2124043714 (DE-He213)s00521-020-05069-1-p DE-627 ger DE-627 rakwb eng 004 VZ Paul, Arati verfasserin aut SSNET: an improved deep hybrid network for hyperspectral image classification 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract Classification is one of the most important task in hyperspectral image processing. In the last few decades, several classification techniques have been introduced. However, most of them could not efficiently extract features from hyperspectral images (HSI). A novel deep learning framework is proposed in this paper which efficiently utilises convolutional neural network (CNN) and spatial pyramid pooling (SPP) for extracting both the spectral–spatial features for classification. The proposed hybrid framework uses principal component analysis (PCA), 3D-CNN, 2D-CNN and SPP. The proposed CNN-based model is applied on three benchmark hyperspectral datasets, and subsequently the performance is compared with state-of-the-art methods in the same field. The obtained results reveal the superiority of the proposed model in effectively classifying HSI. Convolutional neural networks (CNN) Hyperspectral image classification 3D-CNN 2D-CNN Spatial pyramid pooling (SPP) Bhoumik, Sanghamita aut Chaki, Nabendu aut Enthalten in Neural computing & applications Springer London, 1993 33(2020), 5 vom: 16. Juni, Seite 1575-1585 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:33 year:2020 number:5 day:16 month:06 pages:1575-1585 https://doi.org/10.1007/s00521-020-05069-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 33 2020 5 16 06 1575-1585 |
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10.1007/s00521-020-05069-1 doi (DE-627)OLC2124043714 (DE-He213)s00521-020-05069-1-p DE-627 ger DE-627 rakwb eng 004 VZ Paul, Arati verfasserin aut SSNET: an improved deep hybrid network for hyperspectral image classification 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract Classification is one of the most important task in hyperspectral image processing. In the last few decades, several classification techniques have been introduced. However, most of them could not efficiently extract features from hyperspectral images (HSI). A novel deep learning framework is proposed in this paper which efficiently utilises convolutional neural network (CNN) and spatial pyramid pooling (SPP) for extracting both the spectral–spatial features for classification. The proposed hybrid framework uses principal component analysis (PCA), 3D-CNN, 2D-CNN and SPP. The proposed CNN-based model is applied on three benchmark hyperspectral datasets, and subsequently the performance is compared with state-of-the-art methods in the same field. The obtained results reveal the superiority of the proposed model in effectively classifying HSI. Convolutional neural networks (CNN) Hyperspectral image classification 3D-CNN 2D-CNN Spatial pyramid pooling (SPP) Bhoumik, Sanghamita aut Chaki, Nabendu aut Enthalten in Neural computing & applications Springer London, 1993 33(2020), 5 vom: 16. Juni, Seite 1575-1585 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:33 year:2020 number:5 day:16 month:06 pages:1575-1585 https://doi.org/10.1007/s00521-020-05069-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 33 2020 5 16 06 1575-1585 |
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10.1007/s00521-020-05069-1 doi (DE-627)OLC2124043714 (DE-He213)s00521-020-05069-1-p DE-627 ger DE-627 rakwb eng 004 VZ Paul, Arati verfasserin aut SSNET: an improved deep hybrid network for hyperspectral image classification 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract Classification is one of the most important task in hyperspectral image processing. In the last few decades, several classification techniques have been introduced. However, most of them could not efficiently extract features from hyperspectral images (HSI). A novel deep learning framework is proposed in this paper which efficiently utilises convolutional neural network (CNN) and spatial pyramid pooling (SPP) for extracting both the spectral–spatial features for classification. The proposed hybrid framework uses principal component analysis (PCA), 3D-CNN, 2D-CNN and SPP. The proposed CNN-based model is applied on three benchmark hyperspectral datasets, and subsequently the performance is compared with state-of-the-art methods in the same field. The obtained results reveal the superiority of the proposed model in effectively classifying HSI. Convolutional neural networks (CNN) Hyperspectral image classification 3D-CNN 2D-CNN Spatial pyramid pooling (SPP) Bhoumik, Sanghamita aut Chaki, Nabendu aut Enthalten in Neural computing & applications Springer London, 1993 33(2020), 5 vom: 16. Juni, Seite 1575-1585 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:33 year:2020 number:5 day:16 month:06 pages:1575-1585 https://doi.org/10.1007/s00521-020-05069-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 33 2020 5 16 06 1575-1585 |
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Abstract Classification is one of the most important task in hyperspectral image processing. In the last few decades, several classification techniques have been introduced. However, most of them could not efficiently extract features from hyperspectral images (HSI). A novel deep learning framework is proposed in this paper which efficiently utilises convolutional neural network (CNN) and spatial pyramid pooling (SPP) for extracting both the spectral–spatial features for classification. The proposed hybrid framework uses principal component analysis (PCA), 3D-CNN, 2D-CNN and SPP. The proposed CNN-based model is applied on three benchmark hyperspectral datasets, and subsequently the performance is compared with state-of-the-art methods in the same field. The obtained results reveal the superiority of the proposed model in effectively classifying HSI. © Springer-Verlag London Ltd., part of Springer Nature 2020 |
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Abstract Classification is one of the most important task in hyperspectral image processing. In the last few decades, several classification techniques have been introduced. However, most of them could not efficiently extract features from hyperspectral images (HSI). A novel deep learning framework is proposed in this paper which efficiently utilises convolutional neural network (CNN) and spatial pyramid pooling (SPP) for extracting both the spectral–spatial features for classification. The proposed hybrid framework uses principal component analysis (PCA), 3D-CNN, 2D-CNN and SPP. The proposed CNN-based model is applied on three benchmark hyperspectral datasets, and subsequently the performance is compared with state-of-the-art methods in the same field. The obtained results reveal the superiority of the proposed model in effectively classifying HSI. © Springer-Verlag London Ltd., part of Springer Nature 2020 |
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Abstract Classification is one of the most important task in hyperspectral image processing. In the last few decades, several classification techniques have been introduced. However, most of them could not efficiently extract features from hyperspectral images (HSI). A novel deep learning framework is proposed in this paper which efficiently utilises convolutional neural network (CNN) and spatial pyramid pooling (SPP) for extracting both the spectral–spatial features for classification. The proposed hybrid framework uses principal component analysis (PCA), 3D-CNN, 2D-CNN and SPP. The proposed CNN-based model is applied on three benchmark hyperspectral datasets, and subsequently the performance is compared with state-of-the-art methods in the same field. The obtained results reveal the superiority of the proposed model in effectively classifying HSI. © Springer-Verlag London Ltd., part of Springer Nature 2020 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2124043714</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505082943.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230505s2020 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-020-05069-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2124043714</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00521-020-05069-1-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Paul, Arati</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">SSNET: an improved deep hybrid network for hyperspectral image classification</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag London Ltd., part of Springer Nature 2020</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Classification is one of the most important task in hyperspectral image processing. In the last few decades, several classification techniques have been introduced. However, most of them could not efficiently extract features from hyperspectral images (HSI). A novel deep learning framework is proposed in this paper which efficiently utilises convolutional neural network (CNN) and spatial pyramid pooling (SPP) for extracting both the spectral–spatial features for classification. The proposed hybrid framework uses principal component analysis (PCA), 3D-CNN, 2D-CNN and SPP. The proposed CNN-based model is applied on three benchmark hyperspectral datasets, and subsequently the performance is compared with state-of-the-art methods in the same field. 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