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.
In this paper, we proposed a multi-scale convolutional neural network for hyperspectral image classification task. Firstly, compared with conventional convolution, we utilize multi-scale convolutions, which possess larger respective fields, to extract spectral features of hyperspectral image. We design a deep neural network with a multi-scale convolution layer which contains 3 different convolution kernel sizes. Secondly, to avoid overfitting of deep neural network, dropout is utilized, which randomly sleeps neurons, contributing to improve the classification accuracy a bit. In addition, new skills like ReLU in deep learning is utilized in this paper. We conduct experiments on University of Pavia and Salinas datasets, and obtained better classification accuracy compared with other methods.