Similarity-Preserving Deep Features for Hyperspectral Image Classification

Recently, deep learning has been introduced to extract hierarchical features of hyperspectral images (HSls) and achieved good classification performance. However, the previous deep learning based methods only consider the semantic information of individual pixel, which cannot effectively deal with the complex spectral-spatial characteristic of HSls. In this paper, we propose a novel deep learning based framework to learn the similarity-preserving deep features (SPDF) for HSI classification. Specifically, we firstly introduce a deep network that can take pairs of image patches as training samples, and then a loss function is elaborately designed to minimize the feature distance of similar pairs and maximize the feature distance of dissimilar pairs in feature space. Once the deep network is well trained, the SPDF can be obtained by propagating the samples through the trained network. Finally, these features are fed into the support vector machines (SVM) for HSI classification. Experimental results demonstrate the pro-nosed method outperforms other competitive methods.