Hierarchical Similarity Alignment for Domain Adaptive Ship Detection in SAR Images

Ship detection from Synthetic Aperture Radar (SAR) images is a hot topic, but the difficulty in collecting labeled SAR images may hinder the development of deep learning based detection methods. Inspired by the idea of domain adaptation, in this paper, we propose a Hierarchical Similarity Alignment neural Network (HSANet) for ship detection in SAR images, which is a domain adaptive approach with optical remote sensing images as training samples. The kernel target of HSANet is to mine and align both the global structure and the local instance information between SAR and optical images, where two modules, Structural Alignment Module (SAM) and Prototype Alignment Module (PAM), are designed to respectively conduct two hierarchies of alignment process. In general, SAM attempts to extract the global structure similarity which exists in image-level feature representation, while PAM tends to extract the local shape similarity which is instance-level representation. To be specific, SAM is developed by Fourier-based feature alignment, which tries to describe the similar structural relation between optical and SAR images. Meanwhile, PAM is proposed based on the conjoint confidence analysis where the instance-level ship representations of the source and target domains is aligned. SAM and PAM work together to construct a hierarchical domain adaptation network for SAR ship detection. Experiments on several public datasets may indicate the effectiveness of the proposed method.