An improved algorithm for supervised fuzzyC-means clustering of remotely sensed data |
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| Authors: | Zhang Jingxiong Roger P Kirby |
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| Institution: | (1) Laboratory for information Engineering in Surveying Mapping and Remote Sensing, WTUSM, 129 Luoyu Road, 430079 Wuhan, China |
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| Abstract: | This paper describes an improved algorithm for fuzzyc-means clustering of remotely sensed data, by which the degree of fuzziness of the resultant classification is decreased as
comparing with that by a conventional algorithm: that is, the classification accuracy is increased. This is achieved by incorporating
covariance matrices at the level of individual classes rather than assuming a global one. Empirical results from a fuzzy classification
of an Edinburgh suburban land cover confirmed the improved performance of the new algorithm for fuzzyc-means clustering, in particular when fuzziness is also accommodated in the assumed reference data. |
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| Keywords: | remotely sensed data (images) classification fuzzyc-means clustering fuzzy membership values (FMVs) Mahalanobis distances covariance matrix |
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