Gradient Guided Multiscale Feature Collaboration Networks for Few-Shot Class-Incremental Remote Sensing Scene Classification

Few-shot class-incremental learning has recently received significant research focus in remote sensing scene classification (FSCIL-RSSC). The success of FSCIL-RSSC relies on the robustness of the feature backbone and classifiers. Existing works focus on improving classifier adaptation, but little attention is paid to the importance of backbone robustness on the recognition ability of new class samples’ embeddings. Due to the large distribution shift between old and new classes, FSCIL-RSSC using high-layer (single-scale) features may not adapt flawlessly to new categories. To solve the issue, we put forward a gradient guided multiscale feature collaboration network (G-MFCN) for FSCIL-RSSC. Specifically, we introduce a parallel hierarchy strategy to simultaneously capture the multifeature discriminative information of the same sample. Then, a gradient guide block is designed to automatically pick out the optimal values of different convolution blocks for multifeature fusion. Finally, the classical feature pyramid network is introduced for multiscale fusion to obtain more obvious discriminative features of RSSC. More importantly, our proposed G-MFCN is a simple and adaptable module, which can combine any existing FSCIL frameworks to further improve the optimized classifiers’ effectiveness for the FSCIL-RSSC scenario. Extensive experiments on four benchmarks demonstrate that the proposed G-MFCN achieves significant improvements in comparison to existing FSCIL-RSSC methods.