Deep Hashing Neural Networks for Hyperspectral Image Feature Extraction

Recently, deep learning has been recognized as a powerful tool to extract hierarchical features of hyperspectral images (HSIs). The existing deep learning-based methods exploit label information of land classes as the supervised information to train deep networks. However, considering that HSIs exhibit very complex spectral-spatial characteristic, e.g., the large intraclass variations and small interclass variations, these semantic information (i.e., label information)-based deep networks may not effectively cope with the above problem. In this letter, we propose a novel deep model, named deep hashing neural network (DHNN), to learn similarity-preserving deep features (SPDFs) for HSI classification. First, a well-pretrained network is introduced to simultaneously extract features of a pair of input samples. Second, a novel hashing layer is inserted after the last fully connected layer to transfer the real-value features into binary features, which can significantly speed up the computation for feature distance. Then, a loss function is elaborately designed to minimize the feature distance of similar pairs and maximize the feature distance of dissimilar pairs in Hamming space. Finally, the SPDF extracted by propagating the samples through the trained DHNN are fed into a support vector machine (SVM) classifier for HSI classification. Experimental results on two real HSIs demonstrate that the proposed feature extraction method in conjunction with a linear SVM classifier outperforms other feature extraction methods and competitive classifiers.