A Parallel Gaussian–Bernoulli Restricted Boltzmann Machine for Mining Area Classification With Hyperspectral Imagery

Abstract

In this paper, a novel feature extraction method is proposed for hyperspectral image classification using a Gaussian–Bernoulli restricted Boltzmann machine (GBRBM) in parallel. The proposed approach employs several GBRBMs with different hidden layers to extract deep features from hyperspectral images, which are nonlinear and local invariant. Based on the learned deep features, a logistic regression layer is trained for classification. The proposed approaches are carried out on two public hyperspectral datasets: Pavia University dataset and Salinas dataset, and a new dataset obtained by HySpex imaging spectrometer in the mining area in Xuzhou. The obtained results reveal that the proposed approach offers superior performance compared to traditional classifiers. The advantage of the proposed GBRBM is that it can extract deep features in an unsupervised way and reduce the prediction time by using GPU. In particular, the classification results of the mining area provide valuable suggestions to improve environmental protection.