A Transformer-Based Cross-Resolution Target Recognition Method for SAR Images Based on Feature Alignment and Aggregation
Kailing Tang, Zongyong Cui, Zheng Zhou, Quan Gan, Zongjie Cao · 2024
Synthetic aperture radar (SAR) imagery is susceptible to multiple factors, including radar parameters, imaging mode, and imaging angle. Consequently, the resolution of the training and test data often differs, leading to the failure of existing deep learning-based SAR target recognition methods. To address this challenge, this paper introduces a transformer-based cross-resolution target recognition method for SAR images based on feature alignment and aggregation. The objective is to improve the accuracy of cross-resolution target recognition. Experimental results obtained from OpenSARShip, an openly available dataset with ship images captured at various resolutions, demonstrate the superior performance of the proposed method compared to the current state-of-the-art (SOTA) technique.