Hyperspectral image classification has been recognized as a basic and challenging task in hyperspectral data processing, wherein the rich spectral and spatial information provides an opportunity for the effective description and identification of the surface materials of the earth. There are many parameters in convolutional neural network (CNN). In order to avoid overfitting problem, a large number of training samples are needed in CNN. In addition, the Log-Gabor filtering can effectively extract spatial information, such as the edge and texture, which reduces the difficulty of CNN feature extraction. To leverage the advantages of CNN and Log-Gabor filtering, a hyperspectral image classification method that combines the Log-Gabor filtering and CNN is proposed herein, and two real hyperspectral image datasets are used for comparison experiments. Experimental results show that the proposed method has a higher classification accuracy than that of the traditional support vector machine and CNN.