A New Causal Meta-Learning Framework for Few-Shot SAR Target Classification
Jiaxiang Liu, Xuemeng Hui, Zhunga Liu · 2025
Few-Shot Learning (FSL) methods have made significant advancements in natural optical image recognition. These methods rely on fine-tuning models that have already been extensively trained on large-scale datasets. However, pre-trained knowledge can mislead subsequent recognition processes. In the case of Synthetic Aperture Radar (SAR) FSL tasks, the pretraining stage usually uses self-supervised techniques to mitigate the issue of limited data, which can worsen the misguided judgment resulting from pre-trained knowledge. In this paper, a new framework, named Causal Meta-Learning (CML), is proposed to tackle this issue. Firstly, the FSL task is modeled as a causal graph from a causal inference perspective to clearly identify the bias introduced by pre-trained knowledge. Secondly, two distinct backdoor interventions are designed: intra-task and extra-task adjustments, to sever the direct linkage between pretrained knowledge and feature representations. Finally, a dedicated dataset, named mini-MSTAR, is reconstructed based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset to evaluate our framework. It's important to note that the contributions of CML are independent of existing meta-learning-based FSL methods, enabling CML to enhance all of them. Experiments conducted on mini-MSTAR demonstrate the improved recognition capabilities of several baseline models in 3 -way 1 -shot and 3 -way 5 -shot scenarios.