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Hyperspectral Image Classification Based on Capsule Network

In this paper, we propose two novel classification frameworks for hyperspectral image (HSI) based on capsule network (CapsNet), which could address the drawbacks of convolutional neural network (CNN) and problem of limited training samples by introducing affine transformation matrix. Specifically, the proposed framework first performs the classification of HSI based on spectral information. Second, considering the importance of spatial information for HSI processing, we integrate the spatial and spectral information into the proposed framework to further improve the classification performance. Experimental results on real HSI data demonstrate the effectiveness of the proposed framework.