Wetland remote sensing classification using support vector machine optimized with co-evolutionary algorithm

Abstract

In order to improve the accuracy of support vector machine (SVM) classification of wetland remote sensing images, the selection of kernel function parameters in support vector machines becomes an effective approach. In this paper, Particle Swarm Optimization and Genetic Algorithms (PSO-GA) co-evolutionary algorithm are used to optimize the SVM parameters. Because of the complementarity of evolutionary features between PSO and GA, this algorithm is combined with PSO and GA to improve the convergence speed and realize the optimization of depth and breadth. Experimental results show that SVM with PSO-GA co-evolutionary algorithm can achieve high classification accuracy in finite iteration times compared with existing intelligent optimization algorithms.