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] Gao, Jianqiang Ge, Hongwei Zhang, Yixin Yang, Jieming

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Hyperspectral image Feature extraction Spectral-Gabor space discriminant analysis Classification

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Neural processing letters - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 54(2021), 2 vom: 23. Okt., Seite 909-959
Übergeordnetes Werk:	volume:54 ; year:2021 ; number:2 ; day:23 ; month:10 ; pages:909-959

Links:	Volltext

DOI / URN:	10.1007/s11063-021-10665-w

Katalog-ID:	SPR046720510

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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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700	1		\|a Ge, Hongwei \|4 aut
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700	1		\|a Yang, Jieming \|4 aut
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allfields	10.1007/s11063-021-10665-w doi (DE-627)SPR046720510 (SPR)s11063-021-10665-w-e DE-627 ger DE-627 rakwb eng Li, Li verfasserin aut An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr 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 (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Spectral-Gabor space discriminant analysis (dpeaa)DE-He213 Classification (dpeaa)DE-He213 Gao, Jianqiang (orcid)0000-0002-1989-4943 aut Ge, Hongwei aut Zhang, Yixin aut Yang, Jieming aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 54(2021), 2 vom: 23. Okt., Seite 909-959 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:54 year:2021 number:2 day:23 month:10 pages:909-959 https://dx.doi.org/10.1007/s11063-021-10665-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 54 2021 2 23 10 909-959
spelling	10.1007/s11063-021-10665-w doi (DE-627)SPR046720510 (SPR)s11063-021-10665-w-e DE-627 ger DE-627 rakwb eng Li, Li verfasserin aut An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr 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 (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Spectral-Gabor space discriminant analysis (dpeaa)DE-He213 Classification (dpeaa)DE-He213 Gao, Jianqiang (orcid)0000-0002-1989-4943 aut Ge, Hongwei aut Zhang, Yixin aut Yang, Jieming aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 54(2021), 2 vom: 23. Okt., Seite 909-959 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:54 year:2021 number:2 day:23 month:10 pages:909-959 https://dx.doi.org/10.1007/s11063-021-10665-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 54 2021 2 23 10 909-959
allfields_unstemmed	10.1007/s11063-021-10665-w doi (DE-627)SPR046720510 (SPR)s11063-021-10665-w-e DE-627 ger DE-627 rakwb eng Li, Li verfasserin aut An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr 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 (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Spectral-Gabor space discriminant analysis (dpeaa)DE-He213 Classification (dpeaa)DE-He213 Gao, Jianqiang (orcid)0000-0002-1989-4943 aut Ge, Hongwei aut Zhang, Yixin aut Yang, Jieming aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 54(2021), 2 vom: 23. Okt., Seite 909-959 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:54 year:2021 number:2 day:23 month:10 pages:909-959 https://dx.doi.org/10.1007/s11063-021-10665-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 54 2021 2 23 10 909-959
allfieldsGer	10.1007/s11063-021-10665-w doi (DE-627)SPR046720510 (SPR)s11063-021-10665-w-e DE-627 ger DE-627 rakwb eng Li, Li verfasserin aut An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr 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 (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Spectral-Gabor space discriminant analysis (dpeaa)DE-He213 Classification (dpeaa)DE-He213 Gao, Jianqiang (orcid)0000-0002-1989-4943 aut Ge, Hongwei aut Zhang, Yixin aut Yang, Jieming aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 54(2021), 2 vom: 23. Okt., Seite 909-959 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:54 year:2021 number:2 day:23 month:10 pages:909-959 https://dx.doi.org/10.1007/s11063-021-10665-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 54 2021 2 23 10 909-959
allfieldsSound	10.1007/s11063-021-10665-w doi (DE-627)SPR046720510 (SPR)s11063-021-10665-w-e DE-627 ger DE-627 rakwb eng Li, Li verfasserin aut An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr 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 (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Spectral-Gabor space discriminant analysis (dpeaa)DE-He213 Classification (dpeaa)DE-He213 Gao, Jianqiang (orcid)0000-0002-1989-4943 aut Ge, Hongwei aut Zhang, Yixin aut Yang, Jieming aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 54(2021), 2 vom: 23. Okt., Seite 909-959 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:54 year:2021 number:2 day:23 month:10 pages:909-959 https://dx.doi.org/10.1007/s11063-021-10665-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 54 2021 2 23 10 909-959
language	English
source	Enthalten in Neural processing letters 54(2021), 2 vom: 23. Okt., Seite 909-959 volume:54 year:2021 number:2 day:23 month:10 pages:909-959
sourceStr	Enthalten in Neural processing letters 54(2021), 2 vom: 23. Okt., Seite 909-959 volume:54 year:2021 number:2 day:23 month:10 pages:909-959
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Hyperspectral image Feature extraction Spectral-Gabor space discriminant analysis Classification
isfreeaccess_bool	false
container_title	Neural processing letters
authorswithroles_txt_mv	Li, Li @@aut@@ Gao, Jianqiang @@aut@@ Ge, Hongwei @@aut@@ Zhang, Yixin @@aut@@ Yang, Jieming @@aut@@
publishDateDaySort_date	2021-10-23T00:00:00Z
hierarchy_top_id	270932607
id	SPR046720510
language_de	englisch
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author	Li, Li
spellingShingle	Li, Li misc Hyperspectral image misc Feature extraction misc Spectral-Gabor space discriminant analysis misc Classification An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image
authorStr	Li, Li
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topic_title	An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image Hyperspectral image (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Spectral-Gabor space discriminant analysis (dpeaa)DE-He213 Classification (dpeaa)DE-He213
topic	misc Hyperspectral image misc Feature extraction misc Spectral-Gabor space discriminant analysis misc Classification
topic_unstemmed	misc Hyperspectral image misc Feature extraction misc Spectral-Gabor space discriminant analysis misc Classification
topic_browse	misc Hyperspectral image misc Feature extraction misc Spectral-Gabor space discriminant analysis misc Classification
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image
ctrlnum	(DE-627)SPR046720510 (SPR)s11063-021-10665-w-e
title_full	An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image
author_sort	Li, Li
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author_browse	Li, Li Gao, Jianqiang Ge, Hongwei Zhang, Yixin Yang, Jieming
container_volume	54
format_se	Elektronische Aufsätze
author-letter	Li, Li
doi_str_mv	10.1007/s11063-021-10665-w
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title_sort	effective feature extraction approach based on spectral-gabor space discriminant analysis for hyperspectral image
title_auth	An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image
abstract	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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title_short	An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image
url	https://dx.doi.org/10.1007/s11063-021-10665-w
remote_bool	true
author2	Gao, Jianqiang Ge, Hongwei Zhang, Yixin Yang, Jieming
author2Str	Gao, Jianqiang Ge, Hongwei Zhang, Yixin Yang, Jieming
ppnlink	270932607
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doi_str	10.1007/s11063-021-10665-w
up_date	2024-07-04T00:04:25.006Z
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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).</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hyperspectral image</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature extraction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spectral-Gabor space discriminant analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Classification</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gao, Jianqiang</subfield><subfield code="0">(orcid)0000-0002-1989-4943</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ge, Hongwei</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Yixin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Jieming</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural processing letters</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994</subfield><subfield code="g">54(2021), 2 vom: 23. 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