Performance Analysis of GA and PSO based Feature Selection Techniques for Improving Classification Accuracy in Remote Sensing Images

Abstract

Background/Objectives: - Feature Selection is applicable to decrease the number of features in various applications wherein the data include hundreds and thousands of features. The objective of this study is to choose Genetic Algorithm for feature selection to obtain better fitness function. Methods/Statistical Analysis: Particle Swarm Optimization (PSO) approach is used for selecting the subset from the combination of texture based features and providing the better fitness values. In this paper PSO is used to obtain the feature sets and the performance is compared with genetic algorithm. Support vector machine classifier is used to improve the classification accuracy. Findings: The experimental results shows that PSO overall accuracy is improved to LISS IV 1.7%, 1.4% and 2.9% and the kappa coefficient is improved to 0.06%, 0.012% and 0.39% as compared to GA. Application/Improvements: The Fitness value obtained by GA is more complex and not accurate. To reduce the complexity and increase the accuracy Particle Swarm optimization is used. Hence PSO improved the quality of texture based images.