Accuracy Assessment of MOGA-SVM Method Comparing with Some Supervised and Unsupervised Classification Methods

In 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 compares a new method that combines a recently proposed multi-objective fuzzy clustering scheme with support vector machine (SVM) classifier with four unsupervised and supervised methods like maximum likelihood (ML), SVM, fuzzy c-means (FCM), and k-means (KM). The multi-objective technique is first used to produce a set of non-dominated solutions. The non-dominated 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. However, results demonstrating that supervised classification methods is better than unsupervised methods but new method (MOGA-SVM) shows the best result among other clustering methods. Moreover, a TM satellite image of Qaem Shahr, Iran has been classified using the proposed technique to establish its utility.