A Band Selection Method for Hyperspectral Image Classification based on Improved Particle Swarm Optimization

With the development of spectral imaging technology, it makes hyperspectral imagery widely used. According to the features of multiple bands and the strong mutual correlation among these bands, this paper presents a band selection method for hyperspectral imagery classification based on improved PSO (Particle Swarm Optimization). First of all, we use information divergence to describe the correlation of the bands, then build the information divergence matrix to make the classification of subspaces. Secondly, we construct the fitness function of the algorithm with the band information and categories of the Bhattacharyya distance (B distance) to improve the inertia weight updating method in PSO. Finally, based on the AVIRIS hyperspectral imagery and compared with existing method to conduct experiments, the average classification accuracy of the proposed method is 81.36%, which is distinctly improved 0.91% compared with the existed method. Meanwhile, the proposed method has a significantly faster convergence speed during the process of the band selection. Therefore, the experimental results verify the effectiveness of the proposed method in this paper.