Attribute profile based feature space discriminant analysis for spectral-spatial classification of hyperspectral images

An initial feature reduction is necessary to reduce the data dimensionality before applying attribute filters to hyperspectral images. Unsupervised methods such as principal component analysis are not good choices for classification purposes. On the other hand, supervised methods such as linear discriminant analysis have no good efficiency in small sample size situations. In this article, we propose to extract features using feature space discriminant analysis (FSDA), which has been recently proposed in 2015. FSDA only uses the spectral information and ignores the spatial information. In this paper, we overcome this indigenous disadvantage of FSDA and develop FSDA for spectral-spatial classification of hyperspectral images. Our proposed method, called attribute profile based feature space discriminant analysis (APFSDA), extracts spatial features with high class discrimination ability and as little as redundant information. The experimental results on several real hyperspectral images show the superiority of APFSDA compared to some state-of-the-art spectral-spatial classification methods.