Unsupervised classification of polarimetric synthetic aperture Radar images using fuzzy clustering and EM clustering
Five clustering techniques are compared by classifying a polarimetric synthetic aperture radar image. The pixels are complex covariance matrices, which are known to have the complex Wishart distribution. Two techniques are fuzzy clustering algorithms based on the standard /spl lscr//sub 1/ and /spl...
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| Veröffentlicht in: | IEEE transactions on geoscience and remote sensing 2005-03, Vol.43 (3), p.519-527 |
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| container_title | IEEE transactions on geoscience and remote sensing |
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| creator | Kersten, P.R. Jong-Sen Lee Ainsworth, T.L. |
| description | Five clustering techniques are compared by classifying a polarimetric synthetic aperture radar image. The pixels are complex covariance matrices, which are known to have the complex Wishart distribution. Two techniques are fuzzy clustering algorithms based on the standard /spl lscr//sub 1/ and /spl lscr//sub 2/ metrics. Two others are new, combining a robust fuzzy C-means clustering technique with a distance measure based on the Wishart distribution. The fifth clustering technique is an application of the expectation-maximization algorithm assuming the data are Wishart. The clustering algorithms that are based on the Wishart are demonstrably more effective than the clustering algorithms that appeal only to the /spl lscr//sub p/ norms. The results support the conclusion that the pixel model is more important than the clustering mechanism. |
| doi_str_mv | 10.1109/TGRS.2004.842108 |
| format | Article |
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The pixels are complex covariance matrices, which are known to have the complex Wishart distribution. Two techniques are fuzzy clustering algorithms based on the standard /spl lscr//sub 1/ and /spl lscr//sub 2/ metrics. Two others are new, combining a robust fuzzy C-means clustering technique with a distance measure based on the Wishart distribution. The fifth clustering technique is an application of the expectation-maximization algorithm assuming the data are Wishart. The clustering algorithms that are based on the Wishart are demonstrably more effective than the clustering algorithms that appeal only to the /spl lscr//sub p/ norms. 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The pixels are complex covariance matrices, which are known to have the complex Wishart distribution. Two techniques are fuzzy clustering algorithms based on the standard /spl lscr//sub 1/ and /spl lscr//sub 2/ metrics. Two others are new, combining a robust fuzzy C-means clustering technique with a distance measure based on the Wishart distribution. The fifth clustering technique is an application of the expectation-maximization algorithm assuming the data are Wishart. The clustering algorithms that are based on the Wishart are demonstrably more effective than the clustering algorithms that appeal only to the /spl lscr//sub p/ norms. The results support the conclusion that the pixel model is more important than the clustering mechanism.</description><subject>Algorithms</subject><subject>Classification</subject><subject>Clustering</subject><subject>Clustering algorithms</subject><subject>Covariance matrix</subject><subject>Fuzzy</subject><subject>Fuzzy clustering</subject><subject>Fuzzy logic</subject><subject>Fuzzy set theory</subject><subject>Image classification</subject><subject>Polarimetric synthetic aperture radar</subject><subject>polarimetric synthetic aperture radar (POLSAR)</subject><subject>Probability distribution</subject><subject>Robustness</subject><subject>Statistical analysis</subject><subject>Statistical distributions</subject><subject>Synthetic aperture radar</subject><subject>synthetic aperture radar (SAR)</subject><subject>Testing</subject><issn>0196-2892</issn><issn>1558-0644</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqNkcFrFTEQxoMo-Ky9F7wsHuxpnzNJNrs5SqlVqAi1PYckO1tT9u0-k6zw-tc3yxMUD-JphuH3DTPfx9gZwhYR9Pvbq5tvWw4gt53kCN0ztsGm6WpQUj5nG0Ctat5p_pK9SukBAGWD7Ybluykte4o_Q6K-8qNNKQzB2xzmqZqHaj-PNoYd5Rh8lQ5T_k65dLZI8hKpurG9jVXY2XtK1ZLCdF8Ny-PjoaxaUqa4DuzUV5df_pi8Zi8GOyY6_VVP2N3Hy9uLT_X116vPFx-uay8byDVXKHqntPeq86hb3vROAA2A6FBCK60TjSOUquWohbdeu9JY50h6KZ04YefHvfs4_1goZbMLydM42onmJZlOK44CBC_ku3-SxTktlcT_AKEF7EQB3_4FPsxLnMq7plOtkuvFBYIj5OOcUqTB7IvZNh4MglljNWusZo3VHGMtkjdHSSCi37jQSnApngCWDp_n</recordid><startdate>20050301</startdate><enddate>20050301</enddate><creator>Kersten, P.R.</creator><creator>Jong-Sen Lee</creator><creator>Ainsworth, T.L.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| identifier | ISSN: 0196-2892 |
| ispartof | IEEE transactions on geoscience and remote sensing, 2005-03, Vol.43 (3), p.519-527 |
| issn | 0196-2892 1558-0644 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Classification Clustering Clustering algorithms Covariance matrix Fuzzy Fuzzy clustering Fuzzy logic Fuzzy set theory Image classification Polarimetric synthetic aperture radar polarimetric synthetic aperture radar (POLSAR) Probability distribution Robustness Statistical analysis Statistical distributions Synthetic aperture radar synthetic aperture radar (SAR) Testing |
| title | Unsupervised classification of polarimetric synthetic aperture Radar images using fuzzy clustering and EM clustering |
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