用雷达图与水位数据训练物理引导神经网络，精准识别洪水范围。
A physics-guided neural network for flooding area detection using SAR imagery and local river gauge observations
- 融合哨兵1号影像与河流水位数据，构建物理约束的神经网络
- 洪水区交并比最高达0.89，非洪水区达0.96，优于无监督方法
- 特别适合低水位时的洪水检测，提升遥感监测可靠性
河流流域的洪水范围与水位观测密切相关，水位越高，淹没面积越大。由于合成孔径雷达（SAR）具备穿透云层的能力，常被用于通过多种方法（从简单阈值到深度学习模型）估算洪水范围。本文提出一种物理引导的神经网络用于洪水范围检测，输入为哨兵1号时序影像及对应影像的河流水位数据。采用预测洪水总面积与本地水位观测之间的皮尔逊相关系数作为损失函数。在五个不同研究区域验证了该方法的有效性，将预测水体图与基于数字高程模型和光学卫星图像获取的参考水体图进行对比。模型在水体类别的最高交并比（IoU）达到0.89，非水体类别达0.96。此外，与其它无监督方法相比，本方法在河流低水位时期的SAR影像上表现更优。
原文摘要 · Abstract (English)
The flooding extent area in a river valley is related to river gauge observations. The higher the water elevation, the larger the flooding area. Due to synthetic aperture radar\textquoteright s (SAR) capabilities to penetrate through clouds, radar images have been commonly used to estimate flooding extent area with various methods, from simple thresholding to deep learning models. In this study, we propose a physics-guided neural network for flooding area detection. Our approach takes as input data the Sentinel 1 time-series images and the water elevations in the river assigned to each image. We apply the Pearson correlation coefficient between the predicted sum of water extent areas and the local water level observations of river water elevations as the loss function. The effectiveness of our method is evaluated in five different study areas by comparing the predicted water maps with reference water maps obtained from digital terrain models and optical satellite images. The highest Intersection over Union (IoU) score achieved by our models was 0.89 for the water class and 0.96 for the non-water class. Additionally, we compared the results with other unsupervised methods. The proposed neural network provided a higher IoU than the other methods, especially for SAR images registered during low water elevation in the river.
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