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<p>Accurate river extraction is crucial for agricultural irrigation, water conservancy planning, and flood warning. To mitigate the issues of excessive detail loss and scarcity of labeled data in existing encoder-decoder networks, we propose a non-uniform sampling method combined with graph-based semi-supervised learning to leverage unlabeled data effectively. The method samples more points in high-frequency regions (e.g., river edges) and fewer in low-frequency regions, followed by bilinear interpolation for feature fusion. Experimental results on the Gaofen-2 dataset demonstrate that our method improves Unet, Linknet, and DeeplabV3 by 0.9, 1.5, and 1.6% in accuracy, and by 1.7, 2.9, and 1.9% in IoU, respectively. With semi-supervised learning, using all unlabeled data boosts pixel accuracy by 5.0% and IoU by 9.3%. Additionally, evaluations on the OpenEarthMap dataset and comparisons with state-of-the-art SSL methods further confirm the robustness and generalization capability of our framework.</p>

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River extraction from high-resolution remote sensing images based on non-uniform sampling and semi-supervised learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月31日

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  • Kun Wang,
  • Lin Han,
  • Liangzhi Li

摘要

Accurate river extraction is crucial for agricultural irrigation, water conservancy planning, and flood warning. To mitigate the issues of excessive detail loss and scarcity of labeled data in existing encoder-decoder networks, we propose a non-uniform sampling method combined with graph-based semi-supervised learning to leverage unlabeled data effectively. The method samples more points in high-frequency regions (e.g., river edges) and fewer in low-frequency regions, followed by bilinear interpolation for feature fusion. Experimental results on the Gaofen-2 dataset demonstrate that our method improves Unet, Linknet, and DeeplabV3 by 0.9, 1.5, and 1.6% in accuracy, and by 1.7, 2.9, and 1.9% in IoU, respectively. With semi-supervised learning, using all unlabeled data boosts pixel accuracy by 5.0% and IoU by 9.3%. Additionally, evaluations on the OpenEarthMap dataset and comparisons with state-of-the-art SSL methods further confirm the robustness and generalization capability of our framework.