A Global–Local Residual Refinement Framework for Accurate Lake Boundary Delineation in Remote Sensing Imagery.
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| Title: | A Global–Local Residual Refinement Framework for Accurate Lake Boundary Delineation in Remote Sensing Imagery. |
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| Authors: | Yu, Shangyuan1 (AUTHOR) yushangyuan@mail.cgs.gov.cn, Tu, Jienan1 (AUTHOR), Guo, Zhaocheng1 (AUTHOR), He, Peng1 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1919. 28p. |
| Subjects: | Shoreline monitoring, Shorelines, Deep learning, Lakes, Image segmentation, Remote sensing |
| Geographic Terms: | Tibet (China) |
| Abstract: | Highlights: What are the main findings? A global–local residual refinement framework improves lake shoreline delineation by combining global semantic context with patch-level boundary correction. The proposed method achieves the highest Boundary F1 (3-pixel tolerance) score of 0.811 and the lowest mean BDE (13.19 px) among compared semantic segmentation baselines, including U-Net, SegFormer-B0, SegFormer-B4, and OCRNet. What is the implication of the main finding? Strong region-overlap performance does not necessarily guarantee stable shoreline localization in complex alpine environments. Boundary-aware refinement can noticeably improve shoreline extraction in optical remote sensing imagery affected by ice cover, shadows, and fragmented water bodies. Accurate lake boundary extraction from optical remote sensing imagery remains challenging in high-altitude regions such as the Tibetan Plateau due to ice cover, snow, shadows, and spectrally similar backgrounds. Although recent deep learning models achieve strong region-overlap performance, they often fail to ensure stable shoreline localization. To address this issue, we propose a Global–Local Residual Refinement Network (GLR-Net) for boundary-aware lake extraction from remote sensing imagery. The proposed framework first captures large-scale semantic context through a global branch and subsequently performs patch-level residual refinement to improve local shoreline geometry. A global-to-local guidance mechanism is further introduced to incorporate structural priors into local refinement. Experiments on a manually annotated Tibetan Plateau lake dataset demonstrate that the proposed method achieves competitive region-level segmentation performance while improving geometric shoreline accuracy. Compared with representative semantic segmentation baselines, including U-Net, SegFormer-B0, SegFormer-B4, and OCRNet, the proposed method achieves the highest Boundary F1 score of 0.811 under a 3-pixel tolerance and the lowest mean BDE of 13.19 pixels. The results indicate that conventional overlap-based metrics alone are insufficient for evaluating shoreline delineation quality in complex alpine environments. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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