Unsupervised Pixel Classification in Satellite Imagery Using Multiobjective Fuzzy Clustering Combined With SVM Classifier

Abstract

The problem of unsupervised classification 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 paper proposes a novel approach that combines a recently proposed multiobjective fuzzy clustering scheme with support vector machine (SVM) classifier to yield improved solutions. The multiobjective technique is first used to produce a set of nondominated solutions. The nondominated set is then used to find some high-confidence points using a fuzzy voting technique. The SVM classifier is thereafter trained by these high-confidence points. Finally, the remaining points are classified using the trained classifier. Results demonstrating the effectiveness of the proposed technique are provided for numeric remote sensing data described in terms of feature vectors. Moreover, two remotely sensed images of Bombay and Calcutta cities have been classified using the proposed technique to establish its utility.