SAR Image Ship Detection Based on Scene Interpretation

Abstract

Ship detection from SAR images is an important remote sensing application. However, in complex scenes, i.e., the shore or harbor area, traditional ship detection methods cannot disentangle background information from the target ship, and the detection performance drops dramatically. Moreover, the severe coherent speckle noise also challenges ship detection from SAR images. In order to address the aforementioned issues, this paper proposes a SAR image ship detection method based on scene interpretation to improve the performance of ship detection in complex scenes. Firstly, segmentation algorithm based on Mask R-CNN is utilized to interpret the scene into two catalogs, i.e., the sea and the land. Then, ship detection algorithm based on Faster R-CNN is performed on the sea area and the land area respectively. Finally, non-maximum suppression is used to integrate detection results. Experimental results on SAR ship detection dataset illustrate that the proposed method produces high detection accuracy and low false alarm rate in complex scenes.