Monitoring of rivers is of major scientific and societal importance due to the crucial resource they provide to human activities and the threats caused by flood events. Rapid revisit synthetic aperture radar (SAR) sensors such as Sentinel-1 or the future surface water and ocean topography (SWOT) mission are indispensable tools to achieve all-weather monitoring of water bodies at the global scale. Unfortunately, at the spatial resolution of these sensors, the extraction of narrow rivers is extremely difficult without resorting to exogenous knowledge. This article introduces an innovative river segmentation method from SAR images using a priori databases such as the global river widths from Landsat (GRWL). First, a recently proposed linear structure detector is used to produce a map of likely line structures. Then, a limited number of nodes along the prior river centerline are extracted from the exogenous database and used to reconstruct the full river centerline from the detection map. Finally, an innovative conditional random field approach is used to delineate accurately the river extent around its centerline. The proposed method has been tested on several Sentinel-1 images and on simulated SWOT data. Both visual and qualitative evaluations demonstrate its efficiency.

中文翻译：

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使用外源信息从 SAR 图像中提取狭窄河流


河流监测具有重大的科学和社会重要性，因为它们为人类活动提供了重要资源，并造成洪水事件造成的威胁。 Sentinel-1等快速重访合成孔径雷达（SAR）传感器或未来的地表水和海洋地形（SWOT）任务是实现全球范围内水体全天候监测不可或缺的工具。不幸的是，在这些传感器的空间分辨率下，如果不借助外源知识，提取狭窄河流是极其困难的。本文介绍了一种利用先验数据库（例如来自陆地卫星 (GRWL) 的全球河流宽度）根据 SAR 图像进行河流分割的创新方法。首先，最近提出的线性结构检测器用于生成可能的线性结构图。然后，从外源数据库中提取沿先前河流中心线的有限数量的节点，并用于根据检测图重建完整的河流中心线。最后，采用创新的条件随机场方法来准确描绘河流中心线周围的范围。所提出的方法已在多张 Sentinel-1 图像和模拟 SWOT 数据上进行了测试。视觉和定性评估都证明了其效率。