SAR Ship Target Rapid Detection Method Combined with Scene Classification in the Inshore Region

Synthetic Aperture Radar (SAR) image scene is usually large, deep convolutional network for SAR ship target detection usually requires dense sliding window to extract sub-image pre-processing, and then use the target detection network to directly target detection sub-image, the process has a large number of information redundancy, which greatly affects the efficiency of target detection. In the inshore region, there are many land scenes and complex scenes, for the above problems, this paper proposes a fast target detection method(SC-SSD) for SAR ships in the near-shore region combined with scene classification, which mainly consists of two stages: scene classification stage and target detection stage. They consist of Convolutional Neural Network for Scene Classification (SC-CNN) and Single Shot Detector (SSD), respectively. The SC-CNN can quickly and coarsely filter out the sub-images that may contain ships, and then input the filtered sub-images to the SSD network to realize fine-grained ship target detection. The experimental results based on the high-resolution SAR ship detection dataset AIR-SARShip-1.0 show that the proposed method is significantly faster than the traditional ship detection method while maintaining a higher detection accuracy.