Spatial-spectral random patches network for classification of hyperspectral images

Abstract

Random patches network (RPNet) is an emerging deep learning method that can effectively extract the deep features from hyperspectral images. However, this network only relies on spectral bands in feature extraction, failing to make use of the information-rich spatial features. This paper puts forward another variant of the RPNet, named G-RPNet. The proposed network extracts the deep hierarchical Gabor features, with Gabor spatial features as inputs. The extracted deep hierarchical features were stacked to those extracted by the RPNet, and the final feature vectors were classified by the support vector machine (SVM). The integrated feature vectors inherit the merits of the deep hierarchical features of both RPNet and G-RPNet, laying a solid basis for the classification of hyperspectral images. Experiments were conducted on two real hyperspectral images (Indian Pines and Pavia University) from agricultural and urban areas. The results prove the superiority of the proposed method in the classification of hyperspectral images over some recent shallow and deep spatial-spectral classification techniques.