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

Wei-Lun Tseng, Yi-Lun Wu, Ming-Chun Lee, Hong-Han Shuai

Abstract: 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 (SA) that adapts to images of varying image sizes for masked image modeling (MIM), enabling the model to learn multiscale features and reconstruct fine-grained details lost during upscaling; 2) a foreground-background balanced masking (FBBM) strategy that independently handles the ship region and its surrounding region, ensuring the ship area is neither overmasked nor undermasked; 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.

External IDs:dblp:journals/lgrs/TsengWLS25