MMI-ML: Maximize Mutual Information between Different Views for Few-Shot Remote Sensing Image Classification

Few-shot learning is widely applied in the current stage for remote sensing image classification to use prior knowledge to identify new classes faster. However, since existing few-shot remote sensing image classification methods only process the feature vectors extracted from the complete image, ignoring the localized knowledge in the input incorporated into the target can better utilize the contextual information to capture more local details. To address these problems, we propose a metric learning framework based on maximizing mutual information between different views (MMI-ML). Specifically, we introduce a self-supervised model to train an embedding network with enhanced feature representation by maximizing mutual information of global features and local features at different scales. In addition, we design a new embedding network to make it more appropriate for the self-supervised model. Finally, we devise a new loss function in the training stage, which can effectively speed up the convergence of the model. We conduct comparative experiments on three public remote sensing datasets, and the experimental results show that the classification accuracy of the MMI-ML framework is improved by up to 3.22%.