In the unknown environment, the automatic identification and classification of unmanned aerial vehicle (UAV) landing landforms are of great significance. The traditional natural scene classification uses the information of the middle- and the low-level features, but the UAV landing landform image has complex scene and rich information, which needs high-level semantic features to express more accurate information. A landform image classification algorithm based on discrete cosine transform (DCT) and deep network is proposed. First, the advantage of DCT energy concentration is introduced into the efficient feature representation of convolutional neural network (CNN) to reduce the dimensionality and computational complexity. Then a 14-layer feature learning network is constructed based on the characteristics of landform image, and the CNN structure is improved. Finally, the deep features are input into the support vector machine (SVM) to complete the image classification quickly and accurately. Experimental results show that the algorithm reduces data redundancy and training time greatly, and can automatically learn high-level semantic features. The features extracted by the proposed algorithm have better feature expressions and effectively improve the image classification accuracy.