SAR-to-Infrared domain adaptation for maritime surveillance with limited data

Deep Learning (DL) algorithms need extensive amounts of data for classification tasks, which can be costly in specialized fields like maritime monitoring. To address data scarcity, we propose a fine-tuning approach leveraging complementary Infrared (IR) and Synthetic Aperture Radar (SAR) datasets. We evaluated our method using the ISDD, HRSID, and FuSAR datasets, employing VGG16 as a shared backbone integrated with Faster R-CNN (for ship detection) and a three-layer classifier (for ship classification). The results showed significant improvements in IR ship detection (mAP: +20%; Recall: +17%) and modest but consistent gains in SAR ship detection tasks (F1-score: +3%, Recall: +1%, mAP: +1%). Our findings highlight the effectiveness of domain adaptation in improving DL’s performance under limited data conditions.