Spectra-spatial Graph-based Deep Restricted Boltzmann Networks for Hyperspectral Image Classification

The classification of hyperspectral images (HSI) is a challenging task due to the imbalance between the high dimensionality of spectral features, i.e., the large number of spectral bands and the scarcity of labeled training samples. Moreover, the curse of dimensionality problem deteriorates the classification rate and increases computational complexity. To alleviate these issues, we propose in this paper a novel approach based on deep Restricted Boltzmann Machine (RBM), which improves the spectro-spatial classification of HSI by extracting meaningful features, i.e., finding a better representation of hyperspectral samples. The proposed approach can be divided into three phases; i) spectro-spatial graph construction, ii) deep feature extraction, and iii) spectro-spatial classification. To fully exploit the inherent spatial distribution of the HSI and preserve the spectro-spatial features, the joint similarity measurement encoding both the spectral and spatial features is designed for graph construction. By using the spectro-spatial graph, the proposed RBM can ultimately learn and discriminative representation of hyperspectral samples in the hidden layer. Finally, the extracted deep features vectors are feed as input to Deep Belief Network (DBN) and logistic regression (LR) for the classification. The main advantage of the proposed approach is to learn a better representation of HSI, preserve the deep spectro-spatial features and improve the classification accuracies. Experiments are conducted on two real HSI, i.e., Indian Pines, and Pavia University. The obtained results show that the proposed approach achieves better classification performances compared to other state-of-the-art approaches.