Abstract: High resolution remote sensing images contain abundant information of land use types, in order to solve the problem of extracting feature information from single convolution neural network, a classification model of cascading multi-structure convolution neural networks is proposed. Firstly, We choose CaffeNet(convolutional architecture for fast feature embedding), VGG-S(visual geometry group-slow), VGG-F(visual geometry group-fast)as experimental models, and fine-tune parameter of two full-connected network layers using UC Merced land-use data set. We update the network weight by the stochastic gradient descent(SGD), and then the networks fine-tuned are used as feature extractors, the outputs of the second full-connected layer of the model above as the expression of the images. Finally, by cascading the features of the three networks, the final classification results are obtained using multi-class optimal margin distribution machine (mcODM). The experimental dataset is based on the UC Merced land-use data set. The experimental results show that the method of cascading multi-structure convolution neural network can reach 97.55%, and the accuracy of land use classification is improved by 5.71%, 2.72% and 5.1% respectively compared with pre-trained CNNs.