Particle Swarm Optimization for Object-Based Feature Selection of VHSR Satellite Images

Abstract

With the advent of very high spatial resolution (VHSR) satellite imagery, object-based classification (OBC) has attracted a great deal of attention in the remote sensing community. However, due to the numerous spectral, textural, and structural features which can be extracted for each object, feature selection is needed to address the curse-of-dimensionality issue. In this letter, a new wrapper object-based feature (OBF) selection method was applied in order to select the best combination of spectral, textural, and structural OBFs. In the first step, the image is segmented into objects using the multiresolution segmentation method. Subsequently, OBFs are extracted for each object. Last, optimal OBFs are selected using the proposed method based on particle swarm optimization (PSO) combined with the minimum distance classifier. Two VHSR remotely sensed images, acquired by a WorldView-2 sensor, were used to test the proposed method. The results of the experiment indicated that OBC using optimal OBFs led to significant improvement over the case where all extracted features were used. Furthermore, PSO has a better performance than do the three metaheuristic optimization algorithms: genetic algorithm, artificial bee colony, and honey-bee mating, according to the technique of order of preference by similarity to ideal solution analysis.