An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image
Abstract With the development of remote sensing technology, hyperspectral sensors can record reflectance of ground objects in hundreds of bands, which undoubtedly brings great benefits for hyperspectral remote sensing image that contains abundant information of the land covers. At present, some hype...
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| Autor*in: |
Li, Li [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Neural processing letters - Springer US, 1994, 54(2021), 2 vom: 23. Okt., Seite 909-959 |
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| Übergeordnetes Werk: |
volume:54 ; year:2021 ; number:2 ; day:23 ; month:10 ; pages:909-959 |
| Links: |
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| DOI / URN: |
10.1007/s11063-021-10665-w |
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OLC2078441295 |
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| 520 | |a Abstract With the development of remote sensing technology, hyperspectral sensors can record reflectance of ground objects in hundreds of bands, which undoubtedly brings great benefits for hyperspectral remote sensing image that contains abundant information of the land covers. At present, some hyperspectral images are contaminated by the noise and a lack of spatial information, this phenomenon hinders the improvement of classification accuracy. To further mine the spatial features of hyperspectral image, we propose an effective feature extraction method based on Spectral-Gabor space discriminant analysis (SGDA). The framework of SGDA is roughly divided into four steps: firstly, we obtain $$p_i$$$$\{p_i|1\le i<d\}$$ principal components by PCA, where d denotes the number of features; secondly, we filter the $$p_i$$ principal components with Gabor filter on five different scales and eight different orientations and obtain Gabor spatial features; thirdly, incorporating the spectral features of original hyperspectral data into the Gabor spatial features to form the Spectral-Gabor space features F; finally, we project the fusion features to a low-dimensional subspace, and then maximize the Spectral-Gabor space between-class scatter matrix ($$S_b^{SG}$$) and minimize the Spectral-Gabor space within-class scatter matrix ($$S_w^{SG}$$) at the same time inspired by the idea of Fisher line discriminant analysis. Also, in the above fusion process, the proportion of spectral and spatial information can be controlled by using a penalty factor $$\alpha $$. Experimental results on three hyperspectral datasets show the better classification performance of the SGDA method than the state-of-art methods on the condition of small sample size by Maximum Likelihood Classifier (MLC). | ||
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10.1007/s11063-021-10665-w doi (DE-627)OLC2078441295 (DE-He213)s11063-021-10665-w-p DE-627 ger DE-627 rakwb eng 000 VZ Li, Li verfasserin aut An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image 2021 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 2021 Abstract With the development of remote sensing technology, hyperspectral sensors can record reflectance of ground objects in hundreds of bands, which undoubtedly brings great benefits for hyperspectral remote sensing image that contains abundant information of the land covers. At present, some hyperspectral images are contaminated by the noise and a lack of spatial information, this phenomenon hinders the improvement of classification accuracy. To further mine the spatial features of hyperspectral image, we propose an effective feature extraction method based on Spectral-Gabor space discriminant analysis (SGDA). The framework of SGDA is roughly divided into four steps: firstly, we obtain $$p_i$$$$\{p_i|1\le i<d\}$$ principal components by PCA, where d denotes the number of features; secondly, we filter the $$p_i$$ principal components with Gabor filter on five different scales and eight different orientations and obtain Gabor spatial features; thirdly, incorporating the spectral features of original hyperspectral data into the Gabor spatial features to form the Spectral-Gabor space features F; finally, we project the fusion features to a low-dimensional subspace, and then maximize the Spectral-Gabor space between-class scatter matrix ($$S_b^{SG}$$) and minimize the Spectral-Gabor space within-class scatter matrix ($$S_w^{SG}$$) at the same time inspired by the idea of Fisher line discriminant analysis. Also, in the above fusion process, the proportion of spectral and spatial information can be controlled by using a penalty factor $$\alpha $$. Experimental results on three hyperspectral datasets show the better classification performance of the SGDA method than the state-of-art methods on the condition of small sample size by Maximum Likelihood Classifier (MLC). Hyperspectral image Feature extraction Spectral-Gabor space discriminant analysis Classification Gao, Jianqiang (orcid)0000-0002-1989-4943 aut Ge, Hongwei aut Zhang, Yixin aut Yang, Jieming aut Enthalten in Neural processing letters Springer US, 1994 54(2021), 2 vom: 23. Okt., Seite 909-959 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:54 year:2021 number:2 day:23 month:10 pages:909-959 https://doi.org/10.1007/s11063-021-10665-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 54 2021 2 23 10 909-959 |
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10.1007/s11063-021-10665-w doi (DE-627)OLC2078441295 (DE-He213)s11063-021-10665-w-p DE-627 ger DE-627 rakwb eng 000 VZ Li, Li verfasserin aut An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image 2021 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 2021 Abstract With the development of remote sensing technology, hyperspectral sensors can record reflectance of ground objects in hundreds of bands, which undoubtedly brings great benefits for hyperspectral remote sensing image that contains abundant information of the land covers. At present, some hyperspectral images are contaminated by the noise and a lack of spatial information, this phenomenon hinders the improvement of classification accuracy. To further mine the spatial features of hyperspectral image, we propose an effective feature extraction method based on Spectral-Gabor space discriminant analysis (SGDA). The framework of SGDA is roughly divided into four steps: firstly, we obtain $$p_i$$$$\{p_i|1\le i<d\}$$ principal components by PCA, where d denotes the number of features; secondly, we filter the $$p_i$$ principal components with Gabor filter on five different scales and eight different orientations and obtain Gabor spatial features; thirdly, incorporating the spectral features of original hyperspectral data into the Gabor spatial features to form the Spectral-Gabor space features F; finally, we project the fusion features to a low-dimensional subspace, and then maximize the Spectral-Gabor space between-class scatter matrix ($$S_b^{SG}$$) and minimize the Spectral-Gabor space within-class scatter matrix ($$S_w^{SG}$$) at the same time inspired by the idea of Fisher line discriminant analysis. Also, in the above fusion process, the proportion of spectral and spatial information can be controlled by using a penalty factor $$\alpha $$. Experimental results on three hyperspectral datasets show the better classification performance of the SGDA method than the state-of-art methods on the condition of small sample size by Maximum Likelihood Classifier (MLC). Hyperspectral image Feature extraction Spectral-Gabor space discriminant analysis Classification Gao, Jianqiang (orcid)0000-0002-1989-4943 aut Ge, Hongwei aut Zhang, Yixin aut Yang, Jieming aut Enthalten in Neural processing letters Springer US, 1994 54(2021), 2 vom: 23. Okt., Seite 909-959 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:54 year:2021 number:2 day:23 month:10 pages:909-959 https://doi.org/10.1007/s11063-021-10665-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 54 2021 2 23 10 909-959 |
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10.1007/s11063-021-10665-w doi (DE-627)OLC2078441295 (DE-He213)s11063-021-10665-w-p DE-627 ger DE-627 rakwb eng 000 VZ Li, Li verfasserin aut An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image 2021 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 2021 Abstract With the development of remote sensing technology, hyperspectral sensors can record reflectance of ground objects in hundreds of bands, which undoubtedly brings great benefits for hyperspectral remote sensing image that contains abundant information of the land covers. At present, some hyperspectral images are contaminated by the noise and a lack of spatial information, this phenomenon hinders the improvement of classification accuracy. To further mine the spatial features of hyperspectral image, we propose an effective feature extraction method based on Spectral-Gabor space discriminant analysis (SGDA). The framework of SGDA is roughly divided into four steps: firstly, we obtain $$p_i$$$$\{p_i|1\le i<d\}$$ principal components by PCA, where d denotes the number of features; secondly, we filter the $$p_i$$ principal components with Gabor filter on five different scales and eight different orientations and obtain Gabor spatial features; thirdly, incorporating the spectral features of original hyperspectral data into the Gabor spatial features to form the Spectral-Gabor space features F; finally, we project the fusion features to a low-dimensional subspace, and then maximize the Spectral-Gabor space between-class scatter matrix ($$S_b^{SG}$$) and minimize the Spectral-Gabor space within-class scatter matrix ($$S_w^{SG}$$) at the same time inspired by the idea of Fisher line discriminant analysis. Also, in the above fusion process, the proportion of spectral and spatial information can be controlled by using a penalty factor $$\alpha $$. Experimental results on three hyperspectral datasets show the better classification performance of the SGDA method than the state-of-art methods on the condition of small sample size by Maximum Likelihood Classifier (MLC). Hyperspectral image Feature extraction Spectral-Gabor space discriminant analysis Classification Gao, Jianqiang (orcid)0000-0002-1989-4943 aut Ge, Hongwei aut Zhang, Yixin aut Yang, Jieming aut Enthalten in Neural processing letters Springer US, 1994 54(2021), 2 vom: 23. Okt., Seite 909-959 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:54 year:2021 number:2 day:23 month:10 pages:909-959 https://doi.org/10.1007/s11063-021-10665-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 54 2021 2 23 10 909-959 |
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10.1007/s11063-021-10665-w doi (DE-627)OLC2078441295 (DE-He213)s11063-021-10665-w-p DE-627 ger DE-627 rakwb eng 000 VZ Li, Li verfasserin aut An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image 2021 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 2021 Abstract With the development of remote sensing technology, hyperspectral sensors can record reflectance of ground objects in hundreds of bands, which undoubtedly brings great benefits for hyperspectral remote sensing image that contains abundant information of the land covers. At present, some hyperspectral images are contaminated by the noise and a lack of spatial information, this phenomenon hinders the improvement of classification accuracy. To further mine the spatial features of hyperspectral image, we propose an effective feature extraction method based on Spectral-Gabor space discriminant analysis (SGDA). The framework of SGDA is roughly divided into four steps: firstly, we obtain $$p_i$$$$\{p_i|1\le i<d\}$$ principal components by PCA, where d denotes the number of features; secondly, we filter the $$p_i$$ principal components with Gabor filter on five different scales and eight different orientations and obtain Gabor spatial features; thirdly, incorporating the spectral features of original hyperspectral data into the Gabor spatial features to form the Spectral-Gabor space features F; finally, we project the fusion features to a low-dimensional subspace, and then maximize the Spectral-Gabor space between-class scatter matrix ($$S_b^{SG}$$) and minimize the Spectral-Gabor space within-class scatter matrix ($$S_w^{SG}$$) at the same time inspired by the idea of Fisher line discriminant analysis. Also, in the above fusion process, the proportion of spectral and spatial information can be controlled by using a penalty factor $$\alpha $$. Experimental results on three hyperspectral datasets show the better classification performance of the SGDA method than the state-of-art methods on the condition of small sample size by Maximum Likelihood Classifier (MLC). Hyperspectral image Feature extraction Spectral-Gabor space discriminant analysis Classification Gao, Jianqiang (orcid)0000-0002-1989-4943 aut Ge, Hongwei aut Zhang, Yixin aut Yang, Jieming aut Enthalten in Neural processing letters Springer US, 1994 54(2021), 2 vom: 23. Okt., Seite 909-959 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:54 year:2021 number:2 day:23 month:10 pages:909-959 https://doi.org/10.1007/s11063-021-10665-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 54 2021 2 23 10 909-959 |
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10.1007/s11063-021-10665-w doi (DE-627)OLC2078441295 (DE-He213)s11063-021-10665-w-p DE-627 ger DE-627 rakwb eng 000 VZ Li, Li verfasserin aut An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image 2021 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 2021 Abstract With the development of remote sensing technology, hyperspectral sensors can record reflectance of ground objects in hundreds of bands, which undoubtedly brings great benefits for hyperspectral remote sensing image that contains abundant information of the land covers. At present, some hyperspectral images are contaminated by the noise and a lack of spatial information, this phenomenon hinders the improvement of classification accuracy. To further mine the spatial features of hyperspectral image, we propose an effective feature extraction method based on Spectral-Gabor space discriminant analysis (SGDA). The framework of SGDA is roughly divided into four steps: firstly, we obtain $$p_i$$$$\{p_i|1\le i<d\}$$ principal components by PCA, where d denotes the number of features; secondly, we filter the $$p_i$$ principal components with Gabor filter on five different scales and eight different orientations and obtain Gabor spatial features; thirdly, incorporating the spectral features of original hyperspectral data into the Gabor spatial features to form the Spectral-Gabor space features F; finally, we project the fusion features to a low-dimensional subspace, and then maximize the Spectral-Gabor space between-class scatter matrix ($$S_b^{SG}$$) and minimize the Spectral-Gabor space within-class scatter matrix ($$S_w^{SG}$$) at the same time inspired by the idea of Fisher line discriminant analysis. Also, in the above fusion process, the proportion of spectral and spatial information can be controlled by using a penalty factor $$\alpha $$. Experimental results on three hyperspectral datasets show the better classification performance of the SGDA method than the state-of-art methods on the condition of small sample size by Maximum Likelihood Classifier (MLC). Hyperspectral image Feature extraction Spectral-Gabor space discriminant analysis Classification Gao, Jianqiang (orcid)0000-0002-1989-4943 aut Ge, Hongwei aut Zhang, Yixin aut Yang, Jieming aut Enthalten in Neural processing letters Springer US, 1994 54(2021), 2 vom: 23. Okt., Seite 909-959 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:54 year:2021 number:2 day:23 month:10 pages:909-959 https://doi.org/10.1007/s11063-021-10665-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 54 2021 2 23 10 909-959 |
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An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image |
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An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image |
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Li, Li |
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Li, Li Gao, Jianqiang Ge, Hongwei Zhang, Yixin Yang, Jieming |
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an effective feature extraction approach based on spectral-gabor space discriminant analysis for hyperspectral image |
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An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image |
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Abstract With the development of remote sensing technology, hyperspectral sensors can record reflectance of ground objects in hundreds of bands, which undoubtedly brings great benefits for hyperspectral remote sensing image that contains abundant information of the land covers. At present, some hyperspectral images are contaminated by the noise and a lack of spatial information, this phenomenon hinders the improvement of classification accuracy. To further mine the spatial features of hyperspectral image, we propose an effective feature extraction method based on Spectral-Gabor space discriminant analysis (SGDA). The framework of SGDA is roughly divided into four steps: firstly, we obtain $$p_i$$$$\{p_i|1\le i<d\}$$ principal components by PCA, where d denotes the number of features; secondly, we filter the $$p_i$$ principal components with Gabor filter on five different scales and eight different orientations and obtain Gabor spatial features; thirdly, incorporating the spectral features of original hyperspectral data into the Gabor spatial features to form the Spectral-Gabor space features F; finally, we project the fusion features to a low-dimensional subspace, and then maximize the Spectral-Gabor space between-class scatter matrix ($$S_b^{SG}$$) and minimize the Spectral-Gabor space within-class scatter matrix ($$S_w^{SG}$$) at the same time inspired by the idea of Fisher line discriminant analysis. Also, in the above fusion process, the proportion of spectral and spatial information can be controlled by using a penalty factor $$\alpha $$. Experimental results on three hyperspectral datasets show the better classification performance of the SGDA method than the state-of-art methods on the condition of small sample size by Maximum Likelihood Classifier (MLC). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstractGer |
Abstract With the development of remote sensing technology, hyperspectral sensors can record reflectance of ground objects in hundreds of bands, which undoubtedly brings great benefits for hyperspectral remote sensing image that contains abundant information of the land covers. At present, some hyperspectral images are contaminated by the noise and a lack of spatial information, this phenomenon hinders the improvement of classification accuracy. To further mine the spatial features of hyperspectral image, we propose an effective feature extraction method based on Spectral-Gabor space discriminant analysis (SGDA). The framework of SGDA is roughly divided into four steps: firstly, we obtain $$p_i$$$$\{p_i|1\le i<d\}$$ principal components by PCA, where d denotes the number of features; secondly, we filter the $$p_i$$ principal components with Gabor filter on five different scales and eight different orientations and obtain Gabor spatial features; thirdly, incorporating the spectral features of original hyperspectral data into the Gabor spatial features to form the Spectral-Gabor space features F; finally, we project the fusion features to a low-dimensional subspace, and then maximize the Spectral-Gabor space between-class scatter matrix ($$S_b^{SG}$$) and minimize the Spectral-Gabor space within-class scatter matrix ($$S_w^{SG}$$) at the same time inspired by the idea of Fisher line discriminant analysis. Also, in the above fusion process, the proportion of spectral and spatial information can be controlled by using a penalty factor $$\alpha $$. Experimental results on three hyperspectral datasets show the better classification performance of the SGDA method than the state-of-art methods on the condition of small sample size by Maximum Likelihood Classifier (MLC). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract With the development of remote sensing technology, hyperspectral sensors can record reflectance of ground objects in hundreds of bands, which undoubtedly brings great benefits for hyperspectral remote sensing image that contains abundant information of the land covers. At present, some hyperspectral images are contaminated by the noise and a lack of spatial information, this phenomenon hinders the improvement of classification accuracy. To further mine the spatial features of hyperspectral image, we propose an effective feature extraction method based on Spectral-Gabor space discriminant analysis (SGDA). The framework of SGDA is roughly divided into four steps: firstly, we obtain $$p_i$$$$\{p_i|1\le i<d\}$$ principal components by PCA, where d denotes the number of features; secondly, we filter the $$p_i$$ principal components with Gabor filter on five different scales and eight different orientations and obtain Gabor spatial features; thirdly, incorporating the spectral features of original hyperspectral data into the Gabor spatial features to form the Spectral-Gabor space features F; finally, we project the fusion features to a low-dimensional subspace, and then maximize the Spectral-Gabor space between-class scatter matrix ($$S_b^{SG}$$) and minimize the Spectral-Gabor space within-class scatter matrix ($$S_w^{SG}$$) at the same time inspired by the idea of Fisher line discriminant analysis. Also, in the above fusion process, the proportion of spectral and spatial information can be controlled by using a penalty factor $$\alpha $$. Experimental results on three hyperspectral datasets show the better classification performance of the SGDA method than the state-of-art methods on the condition of small sample size by Maximum Likelihood Classifier (MLC). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image |
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