Deep convolutional neural network structure design for remote sensing image scene classification based on transfer learning

To obtain an ideal scene classification effect when applying a deep convolutional neural network (DCNN) to remote sensing images, a DCNN named ConNet-3F based on transfer learning was constructed. Firstly, the initial 13 layers of VGG16 were used as the main architecture, and then 6 layers were added to the end of it to extract deeper features. The pruned model of VGG16 acted as the feature extractor to extract features from remote sensing images. Subsequently, the parameters trained on the ImageNet were set as initialization. Finally, parameters of all layers were trained on the remote sensing data to obtain the favorable model. The results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset demonstrate that this method achieves higher classification accuracy compared with other mentioned methods. Especially, even if the ratio of training data was reduced on the NWPU-RESISC45 dataset, classification accuracies of the proposed method always stay above 90%.