A Two-Stage Unified Refinement Framework With RS-Specific Priors for Remote Sensing Open-Vocabulary Segmentation
Vision–language pretrained models, particularly CLIP, have demonstrated remarkable zero-shot transfer capabilities across various image-level tasks, catalyzing the advancement of open-vocabulary semantic segmentation (OVSS) in remote sensing (RS). However, the direct deployment of CLIP to the RS domain is inherently constrained by the profound domain shift between terrestrial and overhead perspectives, as well as the intricate geometric heterogeneities regarding scale and orientation. To circumvent these limitations, we propose CDSeg, a robust framework tailored for RSOVSS. Central to this architecture is the dual-domain feature compensation module (DDFCM), which integrates DINOv3 weights, pretrained on large-scale RS benchmarks, to augment CLIP with domain-specific semantic priors, effectively bridging the natural-to-satellite knowledge gap. Furthermore, we introduce a MambaVision-driven cross-feature fine-grained interaction module (CFFIM) to facilitate a unified refinement of spatial and category attributes, leveraging long-range dependency modeling to enhance the model’s discriminative power in unseen environments. To robustly manage the complexities of diverse orientations and scales, CDSeg incorporates a direction-aware rotation strategy and a wavelet-cross-attention-enhanced module (WCAEM) for high-fidelity multiscale feature decoding. Empirical evaluations on four public benchmarks demonstrate that CDSeg achieves state-of-the-art (SOTA) performance, while extensive ablation studies substantiate the synergistic contribution and indispensability of each component.