END-TO-END AUTOMATIC SHIP DETECTION AND RECOGNITION IN HIGH-RESOLUTION GAOFEN-3 SPACEBORNE SAR IMAGES

A framework of end-to-end ship detection and recognition for high-resolution Gaofen-3 (GF3) SAR images is proposed. The framework includes three consecutive stages, namely sea-land segmentation, ship detection and discrimination. First, Otsu-based segmentation is used to exclude land areas. Then, adaptive multi-scale constant false alarm rate (CFAR) algorithm is employed to detect candidate ship target pixels. Subsequently, a convolutional neural network (CNN) is designed to filter out false alarms. The CNN is trained by a in-house built GF3 ship dataset, GF3-FUSAR Ships, which is a matchup dataset of SAR and Automatic identification system (AIS).