Based on the excellent hole convolution performance observed using the obtained image information, we propose a framework for performing hyperspectral image classification based on the dual-channel dilated convolution neural network (DCD-CNN) to improve the classification accuracy. The receptive field of the filters can be expanded via dilated convolution, which effectively avoides the loss of image information and improves the classification accuracy. In this proposed framework, one-dimensional CNN and two-dimensional CNN, exhibiting an empty convolution, are used to extract the spectral and spatial features of the hyperspectral images. Subsequently, these extracted features are combined using a weighted fusion method. Finally, the combined features are input into the support vector machine for performing final classification. The expreimental results on the two commonly used hyperspectral image datasets by the proposed framework are compared with that by the four existing classification methods, showing that the proposed framework exhibits improved classification performance.