Spatial-Spectral Combination Convolutional Neural Network for Hyperspectral Image Classification

Abstract

The great success of deep learning in hyperspectral imagery is attributed to the rapidly developing computational resources. Traditional deep learning methods generally use two different frameworks to learn spatial information and spectral information respectively, then stack deep features for classification. In this paper, a 3-D deep learning model named spatial-spectral combination convolutional neural network (SSCCNN) is proposed to extract discriminative spectral-spatial features. SS-CCNN is an end-to-end network, that is, the raw 3-D cubes can be used as input data without any preprocessing. SSCCN-N can learn the spatial-spectral features and combine shallow features and deep features to alleviate the declining -accuracy phenomenon. Experiments on University of Pavia and Indian Pines data set demonstrate SSCCNN can obtain higher classification accuracy than state-of-the-art methods.