MFT-Reasoning RCNN: A Novel Multi-Stage Feature Transfer Based Reasoning RCNN for Synthetic Aperture Radar (SAR) Ship Detection

Siyu Zhan, Muge Zhong, Yuxuan Yang, Guoming Lu, Xinyu Zhou

Abstract: Conventional ship detection using synthetic aperture radar (SAR) is typically limited to fully focused spatial features of the ship target in SAR images. In this paper, we propose a multi-stage feature transfer (MFT)-based reasoning RCNN (MFT-Reasoning RCNN) to detect ships in SAR images. This algorithm can detect the SAR ship target using the MFT strategy and adaptive global reasoning module over all object regions by exploiting diverse knowledge between the ship and its surrounding elements. Specifically, we first calculate the probability of the simultaneous occurrence of environmental and target elements. Then, taking the environmental and target elements as entities, we construct the relationships between them using an adjacency matrix. Finally, we propose an MFT and use filter feature enhancement in the backbone layer to better extract the target features of SAR images and transfer knowledge between datasets. This paper has been tested on more than 10,000 images, and the experimental results demonstrate that our method can effectively detect different-scale ships in SAR images.