Hyperspectral remote sensing image classification based on spectral-spatial feature fusion and PSO algorithm

Abstract

Hyperspectral images(HSI) have rich spectral information and spatial information. In the classification of hyperspectral images, the combination of spectral information and spatial information has become an effective means to obtain good classification results. Specifically, firstly, PCA algorithm is introduced to extract the spectral information of the image. Secondly, a bilateral filter is introduced for each band to extract the spatial information of the image. Thirdly, the image is classified by using SVM to get the final classification result. Considering that the performance of SVM depends on the choice of parameters, particle swarm optimization algorithm (PSO) is combined with the proposed method to find the optimal penalty parameters and kernel parameters. We find that the classification accuracy is effectively improved by using PCA, bilateral filtering and optimizing SVM-PSO parameters on this basis. Experimental results based on one real hyperspectral dataset show that the proposed algorithm can achieve higher classification accuracy in a shorter time than other algorithms.