Few-Shot SAR Ship Image Detection Using Two-Stage Cross-Domain Transfer Learning.
IEEE International Geoscience and Remote Sensing Symposium (IGARSS)(2022)
摘要
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.
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关键词
transfer,ship,learning,few-shot,two-stage,cross-domain
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