Flood emergency mapping is essential for flood management, often requiring near real-time extraction of large-scale flood extents by combining pre- and post-event multi-source remote sensing images. Pre-event optical imagery delineates the normal water extent, providing a benchmark for estimation of post-flood changes. Synthetic aperture radar (SAR) imagery provides rapid and accurate interpretation of flood inundation extent during the rainy period. Post-classification comparison is one of the fundamental methods for flood mapping of multi-source imagery, as the image characteristics of SAR and optical imagery are inherently different. However, the accuracy of flood mapping depends on the accuracy of water body delineation from single temporal imagery, which can lead to error propagation in flood extent determination. Change detection methods can extract flood extent directly from pre- and post-event imagery, but the models need to learn a consistent flood feature description between optical and SAR imagery from a limited flood dataset. Here, we propose an automatic method for flood inundation extent extraction using pre- and post-event multi-source imagery that requires only a small number of pseudo-change labels. The methodology adopts a pseudo-Siamese network framework as a Heterogeneous - Flood Inundation Extraction Network (H-FIENet), to detect flood extent between the pre‐ and post-event heterogeneous images. Spatially inconsistent multi-source pre- and post-event imagery was used to create a pseudo flood dataset, and this dataset was used to transfer water body feature knowledge from the pre-trained model to the flood extraction model using a cross-task knowledge transfer strategy. Pre- and post-event images from different satellite sources and different flood scenarios were used to evaluate the performance of H-FIENet. We found that: (1) The methodology produced an overall accuracy of over 0.94 for flood inundation extraction from multi-source heterogeneous pre- and post-event imagery. (2) H-FIENet can detect both expansion and recession of the flood extent using any dual-temporal imagery. Our work makes it possible to automate time-series flood monitoring from multi-source remote sensing imagery.

中文翻译：

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使用多源卫星图像进行洪水淹没监测：异构图像变化检测的知识传输策略


洪水应急测绘对于洪水管理至关重要，通常需要通过结合事件前后的多源遥感图像来近实时提取大范围洪水范围。事件前的光学图像描绘了正常的水范围，为估计洪水后的变化提供了基准。合成孔径雷达 (SAR) 图像可快速准确地解释雨季洪水淹没范围。由于合成孔径雷达和光学影像的图像特性存在本质差异，分类后比对是多源影像泛洪成图的基本方法之一。然而，洪水测绘的准确性取决于单个时间图像对水体勾画的准确性，这可能导致洪水范围确定中的误差传播。变化检测方法可以直接从事件前后的图像中提取洪水范围，但模型需要从有限的洪水数据集中学习光学和 SAR 图像之间一致的洪水特征描述。在这里，我们提出了一种使用事件前后多源图像来提取洪水淹没范围的自动方法，该方法仅需要少量的伪变化标签。该方法采用伪暹罗网络框架作为异构洪水淹没提取网络（H-FIENet），以检测事件前和事件后异构图像之间的洪水范围。使用空间不一致的多源事件前后图像来创建伪洪水数据集，并使用该数据集使用跨任务知识转移将水体特征知识从预训练模型转移到洪水提取模型战略。 使用来自不同卫星源和不同洪水场景的事件前和事件后图像来评估 H-FIENet 的性能。我们发现：(1) 该方法从多源异构事件前后图像中提取洪水淹没的总体精度超过 0.94。 (2) H-FIENet 可以使用任何双时相图像来检测洪水范围的扩大和衰退。我们的工作使得利用多源遥感图像自动进行时间序列洪水监测成为可能。