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<p>Accurate, real-time flood mapping from synthetic aperture radar image is vital for disaster response but is hindered by a persistent trade-off between model accuracy and computational efficiency. This paper introduces FM-Mamba, a novel network that leverages a state space model to break this bottleneck. Its core innovation is an encoder built with non-causal Mamba blocks, which captures essential long-range spatial context with linear complexity, paired with a parameter-efficient decoder designed for precise boundary recovery. Evaluated on the Sen1Floods11 and S1GFloods benchmarks, FM-Mamba achieves leading segmentation accuracy, matching or exceeding state-of-the-art methods in F1-score and IoU. Crucially, it accomplishes this with only 3.93 million parameters and a drastically reduced computational footprint, demonstrating a superior balance of performance and efficiency that is ideal for operational, real-time flood mapping.</p>

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FM-Mamba: end-to-end non-causal Mamba-based network for efficient flood mapping

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月1日

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  • Binbin Wang,
  • Zijie Chen,
  • Hailin Zou,
  • Anran Yuan,
  • Yuanyuan Pan,
  • Hongfei Guo,
  • Jianqing Li

摘要

Accurate, real-time flood mapping from synthetic aperture radar image is vital for disaster response but is hindered by a persistent trade-off between model accuracy and computational efficiency. This paper introduces FM-Mamba, a novel network that leverages a state space model to break this bottleneck. Its core innovation is an encoder built with non-causal Mamba blocks, which captures essential long-range spatial context with linear complexity, paired with a parameter-efficient decoder designed for precise boundary recovery. Evaluated on the Sen1Floods11 and S1GFloods benchmarks, FM-Mamba achieves leading segmentation accuracy, matching or exceeding state-of-the-art methods in F1-score and IoU. Crucially, it accomplishes this with only 3.93 million parameters and a drastically reduced computational footprint, demonstrating a superior balance of performance and efficiency that is ideal for operational, real-time flood mapping.