Intra- and Cross-Scale Feature Interaction Network for Referring Remote Sensing Image Segmentation

Referring remote sensing image segmentation (RRSIS) is a challenging multimodal task that requires accurate localization and pixel-level segmentation of targets specified by natural-language expressions. Existing RRSIS datasets remain limited in image resolution, scene diversity, and category coverage, which restricts the generalization ability and practical applicability of current methods. To advance this field, we introduce NWPU-Refer, the largest and most diverse bilingual RRSIS dataset to date. It contains 15003 high-resolution images (1024-2048 px) collected from more than 30 countries across five continents, together with 49745 annotated targets covering single-object, multiobject, and no-target scenarios. We further propose the multiscale referring segmentation network (MRSNet), a framework tailored to the characteristics of remote sensing imagery. In particular, MRSNet incorporates an intrascale feature interaction module (IFIM) to refine fine-grained features within each encoder stage and a hierarchical feature interaction module (HFIM) to integrate cross-scale information while preserving spatial structure. Extensive experiments on NWPU-Refer and RRSIS-D demonstrate that MRSNet achieves strong and competitive performance across multiple metrics, especially under strict IoU thresholds and IoU-based evaluation, validating the effectiveness of the proposed dataset and model. The dataset and code are publicly available at https://github.com/CVer-Yang/NWPU-Refer