FUSAR-GPT : A Spatiotemporal Feature-Embedded and Two-Stage Decoupled Visual Language Model for SAR Imagery

TL;DR

FUSAR-GPT is a specialized visual language model for SAR imagery that incorporates geospatial and spatiotemporal features, achieving state-of-the-art performance in remote sensing tasks.

Contribution

The paper introduces FUSAR-GPT, a novel SAR-specific VLM with spatiotemporal feature embedding and a two-stage decoupling strategy, addressing limitations of existing models.

Findings

FUSAR-GPT outperforms baseline models by over 10% on remote sensing benchmarks.

Constructed the first SAR Image-Text-AlphaEarth feature triplet dataset.

Achieved state-of-the-art results in SAR visual-language understanding.

Abstract

Research on the intelligent interpretation of all-weather, all-time Synthetic Aperture Radar (SAR) is crucial for advancing remote sensing applications. In recent years, although Visual Language Models (VLMs) have demonstrated strong open-world understanding capabilities on RGB images, their performance is severely limited when directly applied to the SAR field due to the complexity of the imaging mechanism, sensitivity to scattering features, and the scarcity of high-quality text corpora. To systematically address this issue, we constructed the inaugural SAR Image-Text-AlphaEarth feature triplet dataset and developed FUSAR-GPT, a VLM specifically for SAR. FUSAR-GPT innovatively introduces a geospatial baseline model as a 'world knowledge' prior and embeds multi-source remote-sensing temporal features into the model's visual backbone via 'spatiotemporal anchors', enabling dynamic compensation for the sparse representation of targets in SAR images. Furthermore, we designed a two-stage SFT strategy to decouple the knowledge injection and task execution of large models. The spatiotemporal feature embedding and the two-stage decoupling paradigm enable FUSAR-GPT to achieve state-of-the-art performance across several typical remote sensing visual-language benchmark tests, significantly outperforming mainstream baseline models by over 10%.