Continually Evolved Feature and Classifiers Learning for Long-Tailed Class-Incremental Remote Sensing Scene Classification

Remote sensing data from real-world scenarios manifests a long-tailed distribution, with the continuous emergence of new classes over time. Nevertheless, the existing class-incremental remote sensing classification models neglect the above long-tailed distribution phenomenon, which seriously damages their overall superior performance. Meanwhile, long-tail class-incremental learning developed in other areas focuses only on the classifier decision boundary optimization of the tail-class, while neglecting the robustness of the feature backbone. The feature backbone trained on the base classes causes a serious significant distribution shift for the incremental classes owing to the distributional differences between base and incremental classes. To solve these issues, we propose a continually evolved feature and classifiers learning (CEF-CL) framework for long-tail class-incremental remote sensing scene classification. Specifically, tail-class data are scaled and grafted onto head-class data to diversify the semantic information of the tail-class leveraging the rich context of the head classes, which can improve the generalization of the feature backbone. And then, an adaptive multi-scale feature fusion (AMFF) module is proposed to couple feature maps of head and tail classes scale by scale for generating virtual tail-class features that deeply perceive head-class information, which can further enhance the reliability of classifier decision boundary optimization. Furthermore, examples from old classes are regarded as pseudo-tail classes to participate in incremental learning, which greatly alleviates catastrophic forgetting of old classes. Extensive experiments on two remote sensing benchmarks demonstrate the superiority of the proposed CEF-CL in comparison with existing class-incremental learning.