Active Style-Content Dual-Branch Domain Adaptation for Semi-Supervised SAR Object Detection

Synthetic Aperture Radar (SAR) images offer unique advantages in all-weather, all-day remote sensing, but the high acquisition costs and time-consuming annotation processes limit their widespread implementation. Semi-supervised domain adaptation leverages abundant annotated optical images and a small number of labeled SAR images to achieve great performance on SAR images. However, existing semi-supervised domain adaptation object detection methods typically select SAR domain labeled samples randomly, making it difficult to fully exploit the valuable information and distinctive features inherent in the target domain data. Moreover, there is a significant style and content gap between optical and SAR images, and previous methods have not adapted to them in a task-specific manner. To this end, this paper proposes an active style-content dual-branch domain adaptation method specifically designed for semi-supervised object detection in SAR images. The proposed approach employs Task-aware Active Sampling (TAS) module to select the most valuable SAR samples, addressing inefficiencies in random sampling. Also, we employ a dual-branch framework to address the style and content gaps between optical and SAR images. Multi-layer Feature Alignment (MFA) module ensures style alignment by maintaining consistent feature representations across different visual styles, while Gaussian-SAM Image Fusion (G-SIF) module is employed to integrate content from the source domain into the target domain, effectively bridging the gap between optical and SAR images. Extensive experiments on multiple ship and aircraft datasets demonstrate the exceptional generalization capabilities of our proposed model.