PLDF-S3: Pseudo-Label-Driven Framework for Offshore to Inshore Unsupervised SAR Image Ship Segmentation

Abstract

Recently, unsupervised ship segmentation methods for synthetic aperture radar (SAR) images have achieved promising results in offshore scenes. However, these methods generate a large number of false alarms in inshore scenes. To address this issue, we propose the pseudo-label-driven framework for offshore to inshore SAR image ship segmentation (PLDF-S3), which leverages ship segmentation results from offshore scenes to assist inshore ship segmentation. In particular, to account for the anisotropy of ships, which are characterized by a dominant long-axis direction, we design a directional feature enhancement module (DFEM) in PLDF-S3 to extract ship features with varying orientations. Additionally, due to the diverse size variations of ships in SAR images, we propose a hierarchical context enhancement module (HCEM) to capture ship features at different scales. Experimental results show that the proposed unsupervised PLDF-S3 achieves comparable segmentation performance than several supervised methods under challenging inshore scenarios.