RSMAE: Radiometric Resolution and Scale-Aware Masked Autoencoder for SAR Ship Recognition

Synthetic Aperture Radar (SAR) has emerged as an indispensable tool for maritime surveillance, providing reliable all-weather, day-and-night imaging capabilities. However, automated ship recognition in SAR imagery presents significant challenges, particularly due to variations in patch sizes, with small-size SAR image patches posing the greatest difficulties. To address this challenge, we propose Radiometric Resolution and Scale-aware Masked Autoencoder (RSMAE), a novel framework designed for SAR ship recognition. Our method incorporates three key innovations: (1) a scale-aware augmentation that adapts to images of varying image sizes for masked image modeling, enabling the model to learn multi-scale features and reconstruct fine-grained details lost during upscaling; (2) a foreground-background balanced masking strategy that independently handles the ship region and its surrounding region, ensuring the ship area is neither over-masked nor under-masked; and (3) a radiometric resolution-aware (RadRe-aware) reweighting mechanism that leverages SAR-specific radiometric characteristics to enhance reconstruction of challenging samples. Experimental results on the OpenSARShip dataset demonstrate that the proposed RSMAE consistently outperforms state-of-the-art methods by at least 2.07% in terms of recognition accuracy. These findings highlight the robustness and efficiency of the proposed RSMAE, making it a compelling solution for SAR ship recognition tasks.