FUSAR-R1: A Large-Scale Reasoning Model for Intelligent Interpretation of SAR Images
In recent years, large-scale vision-language models have been driving a paradigm shift in intelligent remote sensing image interpretation. By incorporating textual semantic information, the cognitive expression, semantic understanding, and human-computer interaction capabilities of interpretation models have been significantly improved, achieving initial progress in the field of Synthetic Aperture Radar (SAR) image interpretation. However, SAR images are affected by factors such as coherent imaging mechanisms, complex scattering characteristics, speckle noise interference, and target-background coupling, resulting in complex and variable image features with significant uncertainties and s...
| Authors | Yi Yang, Xiaokun Zhang, Yuxuan Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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