Unsupervised Satellite Image Segmentation by Combining SA Based Fuzzy Clustering with Support Vector Machine

Abstract

Fuzzy clustering is an important tool for unsupervised pixel classification in remotely sensed satellite images. In this article, a Simulated Annealing (SA) based fuzzy clustering method is developed and combined with popular Support vector Machine (SVM) classifier to fine tune the clustering produced by SA for obtaining an improved clustering performance. The performance of the proposed technique has been compared with that of some other well-known algorithms for an IRS satellite image of the city of Kolkata and its superiority has been demonstrated quantitatively and visually.