CROSS-DOMAIN SAR SHIP DETECTION IN STRONG INTERFERENCE ENVIRONMENT BASED ON IMAGE-TO-IMAGE TRANSLATION

The model performance of object detection task may dramatically deteriorate when meeting the new dataset with discrepant data distribution compared with trained images. Especially for Synthetic Aperture Radar (SAR) images, the complicated imaging mechanism and diverse environments probably induce intense changes in image appearance and hurt the detection capability and robustness of models based on deep learning. In this paper, a method of learning strong interference characteristics of SAR images is proposed and conducted to generate artificial SAR images as extra training samples in the downstream task -- object detection to improve the detection accuracy and decrease the missing rate of models. Our approach utilized as a data augmentation strategy without annotation cost is confirmed to be efficacious and reliable by multiple experiments.