Remote Sensing Satellite Images Classification Using Support Vector Machine and Particle Swarm Optimization

This paper introduces a classification system for remote sensing ASTER satellite imagery using SVM and particle swarm optimization (PSO) algorithm. The proposed system starts with the identification of selected area of study. This is followed by a pre-processing phase using mapping polynomial algorithm as geometric correction. Followed by, applying threshold algorithm for image segmentation. Then features are extracted using object based algorithm. Followed by, image classification using SVM and particle swarm optimization(PSO). The PSO is employed as a fast global optimization algorithm instead of using traditional algorithm such as Karush-Kuhn-Tucker conditions. It is implemented and evaluated on real two selected area of interest in the North-Eastern part of the Eastern Desert of Egypt (Halaib Triangle)and (Wadi Shait). The obtained results carried out that the usage of RBF kernel function has the highest classification accuracy ratio as well as Polynomial kernel function.