Using Siamese capsule networks for remote sensing scene classification

The convolutional neural network (CNN) is widely used for image classification because of its powerful feature extraction capability. The key challenge of CNN in remote sensing (RS) scene classification is that the size of data set is small and images in each category vary greatly in position and angle, while the spatial information will be lost in the pooling layers of CNN. Consequently, how to extract accurate and effective features is very important. To this end, we present a Siamese capsule network to address these issues. Firstly, we introduce capsules to extract the spatial information of the features so as to learn equivariant representations. Secondly, to improve the classification accuracy of the model on small data sets, the proposed model utilizes the structure of the Siamese network as embedded verification. Finally, the features learned through Capsule networks are regularized by a metric learning term to improve the robustness of our model. The effectiveness of the model on three benchmark RS data sets is verified by different experiments. Experimental results demonstrate that the comprehensive performance of the proposed method surpasses other existing methods.