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 rand