Spectral and Spatial Discriminant Analysis for Fused Feature Extraction in Hyperspectral Images

The fusion of valuable spectral and spatial features can significantly improve the performance of high resolution hyperspectral images classification. In this paper, we propose a spectral and spatial feature extraction method based on discriminant analysis. To increase the class discrimination, we maximize the between-class scatters and minimize the within-class scatters. To include the spatial information in the feature extraction process, we estimate the spatial scatters in a spatial neighborhood window with multi-scale fashion. We compare our proposed method, which is called spectral-spatial discriminant analysis (SSDA), with some spatial-spectral feature extraction methods included original spectral bands plus Gabor filters, gray level co-occurance matrix (GLCM), and morphology profiles and also with some popular spectral feature extraction methods such as nonparametric weighted feature extraction (NWFE) and locality preserving projection (LPP). Moreover, we compare SSDA with some recently proposed spectral-spatial classification approaches. The experimental results on two real hyperspectral images show the good performance of SSDA compared to the competitor methods.