A background robust learning method for SAR target recognition

Synthetic Aperture Radar (SAR) is a key remote sensing modality widely deployed on airborne and spaceborne platforms for large-scale Earth observation and surveillance. In SAR Automatic Target Recognition (ATR), most existing methods are developed under the assumption that training and testing data follow an Independently and Identically Distributed (IID) setting. However, in practical applications, variations in observation geometry, terrain composition, and environmental conditions frequently violate this assumption, resulting in substantial background-induced distribution shifts. SAR imagery inherently captures not only target scattering characteristics but also complex background-dependent clutter, which can dominate the learned representations and lead to significant performance degradation when background conditions differ from those encountered during training. To address this challenge, a Background Robust Learning (BRL) framework grounded in structural causal modeling is presented to explicitly analyze and mitigate the influence of environmental confounders in SAR target recognition. Firstly, we construct a causal graph to formally describe the confounding pathways introduced by background and latent environmental factors. Subsequently, we employ causal intervention strategies, utilizing backdoor adjustment for observable confounders and frontdoor adjustment for unobservable ones, to eliminate spurious background-related bias in the prediction process. The resulting intervened causal graph effectively removes spurious background-prediction correlations, thereby achieving robust recognition performance under Out-Of-Distribution (OOD) conditions caused by covariate shift in background scenes. Extensive experiments conducted on the EOC-Scene dataset demonstrate that the proposed method achieves consistent performance improvements across multiple complex remote sensing scenes, indicating enhanced generalization capability and robustness to background variations in practical airborne and spaceborne SAR applications.