Synthetic aperture radar (SAR) has great potential for timely monitoring of flood information as it penetrates the clouds during flood events. Moreover, the proliferation of SAR satellites with high spatial and temporal resolution provides a tremendous opportunity to understand the flood risk and its quick response. However, traditional algorithms to extract flood inundation using SAR often require manual parameter tuning or data annotation, which presents a challenge for the rapid automated mapping of large and complex flooded scenarios. To address this issue, we proposed a segmentation algorithm for automatic flood mapping in near-real-time over vast areas and for all-weather conditions by integrating Sentinel-1 SAR imagery with an unsupervised machine learning approach named Felz-CNN. The algorithm consists of three phases: (i) super-pixel generation; (ii) convolutional neural network-based featurization; (iii) super-pixel aggregation. We evaluated the Felz-CNN algorithm by mapping flood inundation during the Yangtze River flood in 2020, covering a total study area of 1,140,300 km². When validated on fine-resolution Planet satellite imagery, the algorithm accurately identified flood extent with producer and user accuracy of 93% and 94%, respectively. The results are indicative of the usefulness of our unsupervised approach for the application of flood mapping. Meanwhile, we overlapped the post-disaster inundation map with a 10-m resolution global land cover map (FROM-GLC10) to assess the damages to different land cover types. Of these types, cropland and residential settlements were most severely affected, with inundation areas of 9,430.36 km² and 1,397.50 km², respectively, results that are in agreement with statistics from relevant agencies. Compared with traditional supervised classification algorithms that require time-consuming data annotation, our unsupervised algorithm can be deployed directly to high-performance computing platforms such as Google Earth Engine and PIE-Engine to generate a large-spatial map of flood-affected areas within minutes, without time-consuming data downloading and processing. Importantly, this efficiency enables the fast and effective monitoring of flood conditions to aid in disaster governance and mitigation globally.

合成孔径雷达 (SAR) 在洪水事件期间穿透云层，因此具有及时监测洪水信息的巨大潜力。此外，具有高时空分辨率的 SAR 卫星的扩散为了解洪水风险及其快速响应提供了巨大的机会。然而，使用SAR提取洪水淹没的传统算法通常需要手动参数调整或数据注释，这对大型复杂洪水场景的快速自动映射提出了挑战。为了解决这个问题，我们提出了一种分割算法，通过将 Sentinel-1 SAR 图像与一种名为 Felz-CNN 的无监督机器学习方法相结合，在广阔的区域和全天候条件下近实时地自动绘制洪水地图。该算法由三个阶段组成：(i) 超像素生成；(ii) 基于卷积神经网络的特征化；(iii) 超像素聚合。我们通过绘制 2020 年长江洪水期间洪水淹没情况对 Felz-CNN 算法进行了评估，研究总面积为 1,140,​​300 平方公里² . 在高分辨率 Planet 卫星图像上进行验证时，该算法准确识别洪水范围，生产者和用户的准确率分别为 93% 和 94%。结果表明了我们的无监督方法在洪水测绘应用中的有用性。同时，我们将灾后淹没地图与分辨率为 10 米的全球土地覆盖图（FROM-GLC10）重叠，以评估对不同土地覆盖类型的损害。在这些类型中，农田和居民区受影响最严重，淹没面积分别为 9,430.36 km ²和 1,397.50 km ²分别为与相关机构统计数据一致的结果。与传统的有监督分类算法需要耗时的数据标注相比，我们的无监督算法可以直接部署到谷歌地球引擎和PIE-Engine等高性能计算平台，在几分钟内生成大面积的受洪水影响区域空间图，无需耗时的数据下载和处理。重要的是，这种效率能够快速有效地监测洪水状况，以帮助全球治理和减轻灾害。