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SVMeFC: SVM Ensemble Fuzzy Clustering for Satellite Image Segmentation

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4 Author(s)
Saha, I. ; Interdiscipl. Centre for Math. & Comput. Modeling, Univ. of Warsaw, Warsaw, Poland ; Maulik, U. ; Bandyopadhyay, S. ; Plewczynski, D.

The problem of unsupervised image segmentation of a satellite image in a number of homogeneous regions can be viewed as the task of clustering the pixels in the intensity space. This letter presents an approach that exploits the capability of some recently proposed fuzzy clustering techniques, as well as support vector machine (SVM) classifiers, to yield improved solutions. All the fuzzy clustering techniques are first used to produce a set of different clustering solutions. Each such solution has been improved by a novel technique based on an SVM classifier. Thereafter, the cluster-based similarity partition algorithm is used to create the final clustering solution from all improved ensemble solutions. Results demonstrating the effectiveness of the proposed technique are provided for numeric remote sensing data described in terms of feature vectors. Moreover, a remotely sensed image of Calcutta City has been segmented using the proposed technique to establish its utility. In addition, the additional information of this letter is given as supplementary at http://sysbio.icm.edu.pl/indra/SVMeFC.html.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:9 ,  Issue: 1 )

Date of Publication:

Jan. 2012

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