Research on Data Sample for SAR Ship Detection

As a kind of microwave remote sensing imaging radar, Synthetic Aperture Radar (SAR) has been widely used in military and civil fields because of its advantages of all-weather and all-time imaging. Data is the "crude oil" to promote the development of deep learning. Aiming at the problem of how to use SAR data efficiently, this paper studies the data samples from different data source, different target sizes and different training sample sizes. Firstly, based on the SAR ship dataset, the expansion of specific samples is completed to increase the diversity of samples used in the experiment. Secondly, through the cross experiment of two data source, Sentinel-1 data has strong universality for different data source models. Further research on GF-3 data verifies that low resolution and small-scale target detection has great challenges, which provides some reference for researchers.