Dilated Convolutional Neural Network for Hyperspectral Image Feature Extraction and Classification

In this paper, a dilated convolutional neural network is proposed for hyperspectral image classification. Compared with other methods, 2-dimension dilated convolution is used for the first time to extract and classify the spatial-spectral features in hyperspectral image processing fields. Firstly, 1-dimension convolution is extended to 2-dimension convolution for spatial-spectral features extraction. Secondly, a dilated convolutional structure is utilized to fuse the multi-scale information, which is used to extract the multi-scale information without loss of resolution. The experiments of University of Pavia were repeated with the method proposed in this paper, and some better results are obtained, which proved the effectiveness of the proposed model.