In this letter, a dense convolutional neural network (DCNN) is proposed for hyperspectral image classification, aiming to improve classification performance by promoting feature reuse and strengthening the flow of features and gradients. In the network, features are learned mainly through designed dense blocks, where feature maps generated in each layer can connect directly to the subsequent layers by a concatenation mode. Experiments are conducted on two well-known hyperspectral image data sets, using the proposed method and four comparable methods. Results demonstrate that overall accuracies of the DCNN reached 97.61 and 99.50% for the respective image data sets, representing an obvious improvement over the accuracies of the compared methods. The study confirms that the DCNN can provide more discriminable features for hyperspectral image classification and can offer higher classification accuracies and smoother classification maps.