PDE-Guided Diverse Feature Learning for SAR Rotated Ship Detection

Detecting ships in Synthetic Aperture Radar (SAR) images poses a complex challenge, with recent progress primarily attributed to the development of rotated detectors. However, existing methods often neglect the crucial influence of inherent characteristics in SAR images, such as common speckle noise. Moreover, a notable gap exists in modeling diverse features, particularly the fusion of rotational and high-frequency features. To address these challenges, this paper introduces a high-accuracy detector called PRDet, which builds on two key innovations: partial differential equation (PDE)-Guided Wavelet Transform (PGWT) and Diverse Feature Learning Block (DFLB). The PGWT enhances high-frequency features, such as edges and textures, while eliminating speckle noise by optimizing wavelet transform with PDE, leveraging the ability of PDE to model local variations and preserve structural details. The DFLB, with strong expressive capability, extracts and fuses multi-form ship features through three branches, enabling more accurate ship localization. Extensive experimental evaluations on the publicly available RSSDD and SRSDD-V1.0 benchmarks demonstrate PRDet’s superiority over other SAR rotated ship detectors. For example, on the RSSDD dataset, PRDet achieves an offshore precision of 0.938 and an mAP of 0.908, confirming its effectiveness for practical maritime surveillance applications.