MSMamba: A Multi-Semantic Mamba Framework for Referring Remote Sensing Image Segmentation.
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| Title: | MSMamba: A Multi-Semantic Mamba Framework for Referring Remote Sensing Image Segmentation. |
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| Authors: | Zhang, Tianxiang1,2 (AUTHOR), Li, Junbai1,2 (AUTHOR), Feng, Yanqiang1,2 (AUTHOR), Wen, Zhaokun1,2 (AUTHOR), Liu, Li1,2 (AUTHOR), Li, Jiangyun1,2 (AUTHOR) leejy@ustb.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1949. 23p. |
| Subjects: | Image segmentation, Decoding algorithms, Software frameworks, Dynamical systems |
| Abstract: | Highlights: What are the main findings? MSMamba introduces a Mamba-driven multi-semantic framework for referring remote sensing image segmentation. The proposed Visual–Text Fine-grained Block (VTFB) and multi-scale fusion decoder (MSFD) significantly enhance fine-grained language grounding and scale-aware boundary refinement. What is the implication of the main finding? State-space modeling provides an efficient, scalable alternative to heavy attention mechanisms for long-range context modeling in large-scale remote sensing imagery. Experiments on four public benchmarks show consistent improvements in segmentation accuracy, especially with long-text and cluttered scene descriptions. Remote sensing referring segmentation aims to extract the exact region of an object in an aerial image based on a natural language description, but it remains challenging because remote sensing scenes cover large areas, many objects look similar, and the descriptions are often long and detailed. Existing attention-based models are computationally expensive on large images and may underuse fine-grained language cues, which can lead to inaccurate or incomplete masks. To address this, we present MSMamba, an efficient framework built on a state space model for stable long-range context modeling over large spatial grids. We further strengthen language grounding by identifying descriptive words in the expression and using them to guide visual features from coarse localization to boundary refinement. In addition, we design a scale-aware decoding strategy that fuses multi-scale features with adaptive gating to better handle severe size variation and thin structures. Experiments on four public benchmarks show that MSMamba consistently improves segmentation quality. On RefSegRS, MSMamba improves Pr@0.8 on the test set by 25.53% and increases mIoU by 6.65%. On RRSIS-HR, MSMamba improves Pr@0.8 by 9.09% and increases mIoU by 3.02%. These results suggest that combining a state space model with structured language guidance and scale-aware fusion is a practical alternative to attention-only designs for remote sensing referring segmentation. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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