SRGFormer: Semantic Role-Guided Graph Reasoning for Referring Remote Sensing Image Segmentation

Referring remote sensing image segmentation (RRSIS) aims to segment a target instance from remote sensing imagery according to a natural-language expression. It provides a flexible way to retrieve and localize specific objects in remote sensing scenes, benefiting intelligent Earth observation applications. Although existing methods have achieved promising progress by strengthening vision-language alignment, most of them still represent the expression as a holistic language feature and rely on convolution-dominated decoding for mask prediction. Such a paradigm tends to entangle target category, inter-object relation, and spatial position cues, making it difficult to distinguish the intended instance from multiple same-class distractors in complex remote sensing scenes. To address this limitation, we propose SRGFormer, a graph reasoning framework for RRSIS. Specifically, a semantic role decomposition (SRD) module decomposes the referring expression into target, relation, and position semantics, providing explicit linguistic priors for instance-level localization. Guided by the decomposed relation semantics, a semantic-relational graph transformer (SRGT) performs relation-aware graph reasoning over fused multi-scale visual features, enabling long-range dependency modeling among spatially distributed candidate instances. Furthermore, a progressive mask refinement (PMR) module continuously injects the decomposed semantic priors into semantic modulation, query initialization, and iterative mask decoding, thereby alleviating semantic fading during mask generation. Extensive experiments demonstrate that SRGFormer achieves substantial improvements on RefSegRS, attaining 66.08% mIoU and 76.93% oIoU (surpassing the prior state of the art by 3.96% and 2.83%, respectively) along with a notable 15.95% gain in Pr@0.7. Experiments on the additional RRSIS-D benchmark further demonstrate the general applicability of our approach, where SRGFormer maintains competitive performance (65.87% mIoU and 24.61% Pr@0.9) against existing methods. These results demonstrate that the proposed framework improves target localization and fine-grained mask prediction in complex remote sensing scenes.