SVMeFC: SVM Ensemble Fuzzy Clustering for Satellite Image Segmentation

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 we...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2012-01, Vol.9 (1), p.52-55
Main Authors: Saha, I., Maulik, U., Bandyopadhyay, S., Plewczynski, D.
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Clustering algorithms
Fuzzy
Fuzzy clustering
Fuzzy logic
Fuzzy set theory
Indexes
Mathematical models
Pixel
Remote sensing
remote sensing image
Satellites
Studies
support vector machine (SVM)
Support vector machines
Training
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Summary: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.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2011.2160150