A classification method of hyperspectral images based on dual channel feature enhancement (DCFE) is proposed to solve the problem of how to extract and use the spatial and spectral information of hyperspectral images more fully when the training samples are limited. First, two channels are designed to capture spectral and spatial features, and 3D convolution is used as a feature extractor in each channel. The feature map from the reduced-dimension spectral channel is fused with the feature map of the spatial channel. Finally, the feature map combining spectral and spatial features is input into the attention module, and feature enhancement is achieved by increasing attention to important information while decreasing interference from irrelevant information. The experimental results show that the proposed method has an overall classification accuracy of 96.57%, 98.15%, 98.95%, and 96.83% on four hyperspectral data sets, including Indian Pines (3% training sample), Pavia University (0.5% training sample), Salinas (0.5% training sample), and Botswana (1.2% training sample), respectively. When compared to the other five hyperspectral classification methods, the proposed method has remarkably improved the classification performance.