Gaussian Meta-Feature Directed Aggregation for Few-Shot SAR Target Detection

Synthetic aperture radar (SAR) targets are often characterised by high manoeuvrability and strong concealment, resulting in scarce and few-shot SAR data. Due to the scarcity and variability of samples causing significant fluctuations in class centers, the sample distribution is challenging to determine, resulting in the model’s inability to accurately represent the potential representative features of few-shot SAR targets. Therefore, we propose a few-shot SAR target detection method based on Gaussian meta-feature directed aggregation (GMDA) using the meta-learning paradigm. Specifically, a Gaussian distribution is constructed on the support branch to estimate the class distribution of the few-shot SAR target and replace the traditional class prototype. Based on this, we propose the feature information maximization module (FIMM) to avoid the bias of the feature information and achieve the directional expression of the features in order to complete the efficient aggregation. Experiments on SRSDD-v1.0 and MSAR-1.0 datasets show that our method almost beats the state-of-the-art methods at this stage and achieves state-of-the-art performance in all settings.