Ship detection in SAR images based on recurrent feature pyramid network and rotatable bounding box

Abstract

Ship detection in synthetic aperture radar (SAR) images plays an important role in remote sensing, but it is still full of challenges in the deep learning area. The primary problem is that ships in SAR images have different sizes and orientations. The off-the-shelf detectors are not able to adapt to the situation. Recurrent feature pyramid networks are presented to detect ships with different sizes especially the small ones. Rotatable region proposal network is used for locating ships with a tighter rectangle. Rotatable anchors with sizes, aspect ratios, and angles are designed according to the distribution of ships in dataset. Multiratio region-of-interest pooling is used for projecting arbitrary-oriented proposals to fixed length vectors. Angle-related intersection-of-unit (ArIoU) is used for evaluating the intersection of rotatable proposals. ArIoU can be an indicator for nonmaximum suppression (NMS) and also is used for preparing negative and positive proposals. A loss function is proposed to compute loss between bounding boxes. The sinusoidal function is used for solving the problem of unstable angle. We also use a dataset called SSDD+ (SAR ship detection dataset plus) to evaluate different methods. Experiments based on SSDD+ show that our method achieves state-of-the-art performance. The dataset and the code will be public at https://zhuanlan.zhihu.com/p/143794468.