FEW-SHOT SAR SHIP IMAGE DETECTION USING TWO-STAGE CROSS-DOMAIN TRANSFER LEARNING

Synthetic Aperture Radar is superior to optical sensors in that it can identify ships at all hours and on all days. Deep learning-based object detection relies on huge amounts of data, yet SAR ship images are challenging to obtain and label. A few-shot cross-domain transfer learning approach for SAR image ship detection is used in this paper. It is divided into two stages: the first uses a large volume of optical remote sensing ship images as the source domain training detection framework, and the second employs SAR ship images and optical remote sensing ship images to create a few-shot balanced subset fine-tuning detection framework. Use a metric learning-based prediction box classifier instead of a fully connected prediction box classifier. When fine-tuning the whole detection frame using the metric learning-based prediction frame classifier, the experiments show that an AP50 of 55.99% can be reached with only 10 SAR ship images.