Spectral-Spatial Classification of Hyperspectral Image Using PCA and Gabor Filtering

Abstract

The combination of spectral information and spatial context is known to be a suitable way in improving classification accuracy for hyperspectral image. In this paper, a novel method using PCA and spatial filtering for the classification of hyperspectral image is proposed. Firstly, PCA is used to extract spectral information from the hyperspectral image. Secondly, spatial filters containing a set of 2-D Gabor filters and rolling guidance filters (RGF) are convolved with the principal components to extract the subtle spatial texture and edge features respectively. Thirdly, the obtained features are concatenated together as a feature cube to be classified by SVM. The proposed method is thus named as PCA-GR. Experimental results on two real hyperspectral image data sets demonstrate the significant advantages of the proposed method over the compared ones.