Scene Adaptive SAR Incremental Target Detection via Context-Aware Attention and Gaussian-Box Similarity Metric

Existing incremental target detection (ITD) methods heavily depend on the diversity of information. When the scene in the new image diverges significantly from the previous training data, the detector’s ability to detect known targets diminishes considerably. The scene information in synthetic aperture radar (SAR) images is closely linked to target types, as targets of the same class often emerge in similar environments. Consequently, various scenes are frequently introduced in conjunction with new classes, posing substantial challenges to the robustness of the SAR incremental target detector. To tackle this issue, this article proposes the context-aware attention (CAA) and the Gaussian-box similarity metric (GBSM) methods to enhance the scene adaptability of SAR incremental target detectors. First, the CAA operates during the feature knowledge transfer stage, which consists of a global relation module and a local attention (LA) module. It integrates the relationship between the target and its contextual information while preserving contextual awareness through knowledge transformation. Second, the GBSM establishes a constraint factor through both 2-D Gaussian modeling and distribution similarity measurement. It further modifies the incremental localization loss to reduce the impact of target-background contrast on the model’s localization capability. We set up multiple data increment scenarios using the MSAR and SAR-Aircraft datasets. Comparative experimental results show that our method achieves better performance. In addition, the time consumption of training was recorded, and comparisons demonstrate that our method also offers advantages in efficiency.