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Deep learning methods have played a more and more important role inhyperspectral image classification. However, the general deep learning methodsmainly take advantage of the information of sample itself or the pairwiseinformation between samples while ignore the intrinsic data structure withinthe whole data. To tackle this problem, this work develops a novel deepmanifold embedding method(DMEM) for hyperspectral image classification. First,each class in the image is modelled as a specific nonlinear manifold and thegeodesic distance is used to measure the correlation between the samples. Then,based on the hierarchical clustering, the manifold structure of the data can becaptured and each nonlinear data manifold can be divided into severalsub-classes. Finally, considering the distribution of each sub-class and thecorrelation between different subclasses, the DMEM is constructed to preservethe estimated geodesic distances on the data manifold between the learned lowdimensional features of different samples. Experiments over three real-worldhyperspectral image datasets have demonstrated the effectiveness of theproposed method.

Deep Manifold Embedding for Hyperspectral Image Classification