Optimizing SAR Target Detection Through the Multi-Stage Pre-Training and Fine-Tuning Strategy

Synthetic Aperture Radar (SAR) plays a crucial role in target detection due to its unique all-weather imaging capabilities. With the advancement of deep learning, numerous SAR target detection models have been proposed. However, the presence of speckle noise, low resolution, and the scarcity of SAR datasets have posed significant challenges to the training of these models. To address this issue, we propose a multi-stage pretraining-finetuning strategy. First, we pretrain the backbone on natural images, followed by fine-tuning the entire model on simulated SAR images, and finally fine-tuning it on real-world SAR data. Experimental results demonstrate that our approach effectively mitigates the gap between natural and SAR images, achieving superior results across various detection granularities.