In order to fully extract the spatial-spectral features of hyperspectral image with limited training samples and improve classification accuracy, a hyperspectral image classification method combining dilated convolution and dense network is proposed. Firstly, a multi-scale dilated feature extraction module is constructed by introducing different numbers of dilated convolutional layers and ordinary convolutional layers to increase the receptive field of model through cascading and extract multi-scale features. Then, the dense connections are established between multi-scale dilated feature extraction modules to achieve feature reuse while alleviating the problem of gradient vanishing. However, there are no dense connections within the modules to avoid the problem of building a deep network with excessive network parameters. Finally, the obtained features are sequentially classified through pooling layers, fully connected layers, and Softmax layers. In addition, this study adds the dropout regularization after the fully connected layer to prevent overfitting. Compared with classical classification methods on the Indian Pines and WHU-Hi-Longkou datasets, our method provides an OA of 98. 75% and 98. 82% , respectively. The experimental results show that the network model designed in this study provides the best classification performance at the limited sample conditions.