Cosine Margin Prototypical Networks for Remote Sensing Scene Classification

In practical applications, remote-sensing scene classification tasks generally exhibit data shift problems. In this situation, images have large data discrepancies, leading to diffused features and performance degradation. To address the data shift problem, we propose cosine margin prototypical networks. Specifically, we adopt a cosine margin to constrain strictly features, generating well-clustered and discriminative features. With the cosine margin, our method can alleviate data discrepancies by obtaining discriminative features and further addresses the data shift problem well. We conduct extensive experiments on various datasets and achieve 0.03%-7.11% higher accuracy than existing methods. Competitive experimental results demonstrate that our method can solve the data shift problems well in remote-sensing scene classification.