Remote sensing image classification based on improved fuzzy c-means

Abstract Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs m...
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Gespeichert in:

Autor*in:	Yu, Jie [verfasserIn] Guo, Peihuang Chen, Pinxiang Zhang, Zhongshan Ruan, Wenbin

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2008

Schlagwörter:	FCM algorithm GK algorithm GG algorithm remote sensing classification

Anmerkung:	© Wuhan University and Springer-Verlag GmbH 2008

Übergeordnetes Werk:	Enthalten in: Geo-spatial information science - Wuhan : Wuhan Univ. Journals Press, 1998, 11(2008), 2 vom: Juni, Seite 90-94
Übergeordnetes Werk:	volume:11 ; year:2008 ; number:2 ; month:06 ; pages:90-94

Links:	Volltext

DOI / URN:	10.1007/s11806-008-0017-8

Katalog-ID:	SPR022480048

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allfields	10.1007/s11806-008-0017-8 doi (DE-627)SPR022480048 (SPR)s11806-008-0017-8-e DE-627 ger DE-627 rakwb eng Yu, Jie verfasserin aut Remote sensing image classification based on improved fuzzy c-means 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Wuhan University and Springer-Verlag GmbH 2008 Abstract Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs much time to accomplish the classification for a large number of data. In order to overcome these shortcomings and increase the classification accuracy, Gustafson-Kessel (GK) and Gath-Geva (GG) algorithms are proposed to improve the traditional FCM algorithm which adopts Euclidean distance norm in this paper. The experimental result shows that these two methods are able to detect clusters of varying shapes, sizes and densities which FCM cannot do. Moreover, they can improve the classification accuracy of remote sensing images. FCM algorithm (dpeaa)DE-He213 GK algorithm (dpeaa)DE-He213 GG algorithm (dpeaa)DE-He213 remote sensing classification (dpeaa)DE-He213 Guo, Peihuang aut Chen, Pinxiang aut Zhang, Zhongshan aut Ruan, Wenbin aut Enthalten in Geo-spatial information science Wuhan : Wuhan Univ. Journals Press, 1998 11(2008), 2 vom: Juni, Seite 90-94 (DE-627)546503136 (DE-600)2390723-X 1993-5153 nnns volume:11 year:2008 number:2 month:06 pages:90-94 https://dx.doi.org/10.1007/s11806-008-0017-8 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2008 2 06 90-94
spelling	10.1007/s11806-008-0017-8 doi (DE-627)SPR022480048 (SPR)s11806-008-0017-8-e DE-627 ger DE-627 rakwb eng Yu, Jie verfasserin aut Remote sensing image classification based on improved fuzzy c-means 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Wuhan University and Springer-Verlag GmbH 2008 Abstract Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs much time to accomplish the classification for a large number of data. In order to overcome these shortcomings and increase the classification accuracy, Gustafson-Kessel (GK) and Gath-Geva (GG) algorithms are proposed to improve the traditional FCM algorithm which adopts Euclidean distance norm in this paper. The experimental result shows that these two methods are able to detect clusters of varying shapes, sizes and densities which FCM cannot do. Moreover, they can improve the classification accuracy of remote sensing images. FCM algorithm (dpeaa)DE-He213 GK algorithm (dpeaa)DE-He213 GG algorithm (dpeaa)DE-He213 remote sensing classification (dpeaa)DE-He213 Guo, Peihuang aut Chen, Pinxiang aut Zhang, Zhongshan aut Ruan, Wenbin aut Enthalten in Geo-spatial information science Wuhan : Wuhan Univ. Journals Press, 1998 11(2008), 2 vom: Juni, Seite 90-94 (DE-627)546503136 (DE-600)2390723-X 1993-5153 nnns volume:11 year:2008 number:2 month:06 pages:90-94 https://dx.doi.org/10.1007/s11806-008-0017-8 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2008 2 06 90-94
allfields_unstemmed	10.1007/s11806-008-0017-8 doi (DE-627)SPR022480048 (SPR)s11806-008-0017-8-e DE-627 ger DE-627 rakwb eng Yu, Jie verfasserin aut Remote sensing image classification based on improved fuzzy c-means 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Wuhan University and Springer-Verlag GmbH 2008 Abstract Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs much time to accomplish the classification for a large number of data. In order to overcome these shortcomings and increase the classification accuracy, Gustafson-Kessel (GK) and Gath-Geva (GG) algorithms are proposed to improve the traditional FCM algorithm which adopts Euclidean distance norm in this paper. The experimental result shows that these two methods are able to detect clusters of varying shapes, sizes and densities which FCM cannot do. Moreover, they can improve the classification accuracy of remote sensing images. FCM algorithm (dpeaa)DE-He213 GK algorithm (dpeaa)DE-He213 GG algorithm (dpeaa)DE-He213 remote sensing classification (dpeaa)DE-He213 Guo, Peihuang aut Chen, Pinxiang aut Zhang, Zhongshan aut Ruan, Wenbin aut Enthalten in Geo-spatial information science Wuhan : Wuhan Univ. Journals Press, 1998 11(2008), 2 vom: Juni, Seite 90-94 (DE-627)546503136 (DE-600)2390723-X 1993-5153 nnns volume:11 year:2008 number:2 month:06 pages:90-94 https://dx.doi.org/10.1007/s11806-008-0017-8 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2008 2 06 90-94
allfieldsGer	10.1007/s11806-008-0017-8 doi (DE-627)SPR022480048 (SPR)s11806-008-0017-8-e DE-627 ger DE-627 rakwb eng Yu, Jie verfasserin aut Remote sensing image classification based on improved fuzzy c-means 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Wuhan University and Springer-Verlag GmbH 2008 Abstract Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs much time to accomplish the classification for a large number of data. In order to overcome these shortcomings and increase the classification accuracy, Gustafson-Kessel (GK) and Gath-Geva (GG) algorithms are proposed to improve the traditional FCM algorithm which adopts Euclidean distance norm in this paper. The experimental result shows that these two methods are able to detect clusters of varying shapes, sizes and densities which FCM cannot do. Moreover, they can improve the classification accuracy of remote sensing images. FCM algorithm (dpeaa)DE-He213 GK algorithm (dpeaa)DE-He213 GG algorithm (dpeaa)DE-He213 remote sensing classification (dpeaa)DE-He213 Guo, Peihuang aut Chen, Pinxiang aut Zhang, Zhongshan aut Ruan, Wenbin aut Enthalten in Geo-spatial information science Wuhan : Wuhan Univ. Journals Press, 1998 11(2008), 2 vom: Juni, Seite 90-94 (DE-627)546503136 (DE-600)2390723-X 1993-5153 nnns volume:11 year:2008 number:2 month:06 pages:90-94 https://dx.doi.org/10.1007/s11806-008-0017-8 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2008 2 06 90-94
allfieldsSound	10.1007/s11806-008-0017-8 doi (DE-627)SPR022480048 (SPR)s11806-008-0017-8-e DE-627 ger DE-627 rakwb eng Yu, Jie verfasserin aut Remote sensing image classification based on improved fuzzy c-means 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Wuhan University and Springer-Verlag GmbH 2008 Abstract Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs much time to accomplish the classification for a large number of data. In order to overcome these shortcomings and increase the classification accuracy, Gustafson-Kessel (GK) and Gath-Geva (GG) algorithms are proposed to improve the traditional FCM algorithm which adopts Euclidean distance norm in this paper. The experimental result shows that these two methods are able to detect clusters of varying shapes, sizes and densities which FCM cannot do. Moreover, they can improve the classification accuracy of remote sensing images. FCM algorithm (dpeaa)DE-He213 GK algorithm (dpeaa)DE-He213 GG algorithm (dpeaa)DE-He213 remote sensing classification (dpeaa)DE-He213 Guo, Peihuang aut Chen, Pinxiang aut Zhang, Zhongshan aut Ruan, Wenbin aut Enthalten in Geo-spatial information science Wuhan : Wuhan Univ. Journals Press, 1998 11(2008), 2 vom: Juni, Seite 90-94 (DE-627)546503136 (DE-600)2390723-X 1993-5153 nnns volume:11 year:2008 number:2 month:06 pages:90-94 https://dx.doi.org/10.1007/s11806-008-0017-8 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2008 2 06 90-94
language	English
source	Enthalten in Geo-spatial information science 11(2008), 2 vom: Juni, Seite 90-94 volume:11 year:2008 number:2 month:06 pages:90-94
sourceStr	Enthalten in Geo-spatial information science 11(2008), 2 vom: Juni, Seite 90-94 volume:11 year:2008 number:2 month:06 pages:90-94
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	FCM algorithm GK algorithm GG algorithm remote sensing classification
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container_title	Geo-spatial information science
authorswithroles_txt_mv	Yu, Jie @@aut@@ Guo, Peihuang @@aut@@ Chen, Pinxiang @@aut@@ Zhang, Zhongshan @@aut@@ Ruan, Wenbin @@aut@@
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author	Yu, Jie
spellingShingle	Yu, Jie misc FCM algorithm misc GK algorithm misc GG algorithm misc remote sensing classification Remote sensing image classification based on improved fuzzy c-means
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topic_title	Remote sensing image classification based on improved fuzzy c-means FCM algorithm (dpeaa)DE-He213 GK algorithm (dpeaa)DE-He213 GG algorithm (dpeaa)DE-He213 remote sensing classification (dpeaa)DE-He213
topic	misc FCM algorithm misc GK algorithm misc GG algorithm misc remote sensing classification
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title_sort	remote sensing image classification based on improved fuzzy c-means
title_auth	Remote sensing image classification based on improved fuzzy c-means
abstract	Abstract Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs much time to accomplish the classification for a large number of data. In order to overcome these shortcomings and increase the classification accuracy, Gustafson-Kessel (GK) and Gath-Geva (GG) algorithms are proposed to improve the traditional FCM algorithm which adopts Euclidean distance norm in this paper. The experimental result shows that these two methods are able to detect clusters of varying shapes, sizes and densities which FCM cannot do. Moreover, they can improve the classification accuracy of remote sensing images. © Wuhan University and Springer-Verlag GmbH 2008
abstractGer	Abstract Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs much time to accomplish the classification for a large number of data. In order to overcome these shortcomings and increase the classification accuracy, Gustafson-Kessel (GK) and Gath-Geva (GG) algorithms are proposed to improve the traditional FCM algorithm which adopts Euclidean distance norm in this paper. The experimental result shows that these two methods are able to detect clusters of varying shapes, sizes and densities which FCM cannot do. Moreover, they can improve the classification accuracy of remote sensing images. © Wuhan University and Springer-Verlag GmbH 2008
abstract_unstemmed	Abstract Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs much time to accomplish the classification for a large number of data. In order to overcome these shortcomings and increase the classification accuracy, Gustafson-Kessel (GK) and Gath-Geva (GG) algorithms are proposed to improve the traditional FCM algorithm which adopts Euclidean distance norm in this paper. The experimental result shows that these two methods are able to detect clusters of varying shapes, sizes and densities which FCM cannot do. Moreover, they can improve the classification accuracy of remote sensing images. © Wuhan University and Springer-Verlag GmbH 2008
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title_short	Remote sensing image classification based on improved fuzzy c-means
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author2	Guo, Peihuang Chen, Pinxiang Zhang, Zhongshan Ruan, Wenbin
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Remote sensing image classification based on improved fuzzy c-means

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