Fine-Grained Recognition for Oriented Ship Against Complex Scenes in Optical Remote Sensing Images

Abstract

Remote sensing ship recognition is widely used in civil and military applications, such as national defense construction, fishery management, and navigation supervision. Unfortunately, limited by the lack of public datasets for fine-grained ship recognition, current studies mainly focus on ship detection or coarse-grained ship recognition, whereas fine-grained recognition task is left out. The main challenges for fine-grained ship recognition include: 1) complex scenes and 2) ship characteristics of arbitrary orientation, dense distribution, and huge scale and appearance variation. Aiming at the challenges above, we propose a novel efficient information reuse network (EIRNet) and establish a public 20-class Dataset for Oriented Ship Recognition (DOSR). In our EIRNet, considering recognition robustness to multiscale ships, a dense feature fusion network (DFF-Net) with two fusion directions is designed to maximize the utilization of multilayer information and reduce information redundancy. Then, the fused feature maps are refined by a dual-mask attention module (DMAM) to improve performance in dense and clutter scenes by enhancing the distinction between ships and suppressing clutter. Furthermore, Mask-RPN improves the efficiency of generating proposals by reusing the attention mask. Finally, we introduce a concept of upper level class to mine interclass relationships, which further improves the recognition accuracy. Extensive experiments demonstrate that our EIRNet achieves state-of-the-art performance on DOSR and another popular public dataset called HRSC2016.