Land Use Classification Based on Multi-structure Convolution Neural Network Features Cascading

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.