Gabor filtering based deep network for hyperspectral image classification

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In this paper, a novel model of Gabor Filtering based Deep Network (GFDN) for hyperspectral image classification is proposed. First, spatial features are extracted via Gabor filtering from the three principal components. Gabor filter can capture physical structures of hyperspectral images, such as specific orientation information. Then, the Gabor features and spectral features are simply staked to form combined features. Finally, high-level features are learnt by a stacked sparse auto-encoder deep network. Since the limited training samples negatively affect the classification performance in deep learning, here, an effective way is designed to simulate more training samples. By using both the real and virtual samples, the parameters of deep network can be better learnt and updated, leading to more robust and accurate classification results. Experiments on the real hyperspectral data set reveal the superior performance of the proposed method over some well-known classification methods.