RSTG-SAM: A Two-Stage Text-Guided SAM-Based Model for Referring Remote Sensing Image Segmentation

Remote sensing images are increasingly used in applications, such as environmental monitoring and urban planning. However, the complexity of these images poses challenges for accurate object segmentation, particularly when guided by referring expressions. In this letter, we propose RSTG-segment anything model (SAM), a novel two-stage framework for referring remote sensing image segmentation (RRSIS), which builds upon the SAM. In the first stage, a multimodal encoder and a transformer-based prompt generator produce a sparse prompt that provides coarse object localization based on fused vision-language features. In the second stage, SAM utilizes this prompt to conduct fine-grained segmentation. We also introduce a lightweight text-guided adapter module for the efficient fine-tuning of SAM and a dual-attention fusion (DAF) module to enhance multimodal alignment. Experiments on the RRSIS-D dataset demonstrate that RSTG-SAM achieves the state-of-the-art performance across multiple metrics, particularly in handling small targets.