Fuzzy Classification of Remote Sensing Images Based on Particle Swarm Optimization

Particle swarm optimization (PSO) is a new evolutionary computing technique that is based on swarm intelligence and was developed through the simulation of simplified social models of bird flocks. Because of its excellent performance, PSO is introduced into image fuzzy classification to get the fuzzy class center adaptively. In this study, the particles in the swarm are constructed and the swarm search strategy is proposed to meet the needs of the fuzzy classification application. Then fuzzy classification of remote sensing images based on PSO is implemented and the PSO method obtains satisfactory results in the classification experiments. Compared with the traditional mean value method and the genetic algorithm (GA) method, the PSO method has higher classification accuracy. And the PSO method needs less training time than the GA method. Therefore, fuzzy classification of remote sensing images based on PSO is an efficient and promising classification method.