Xia Hao, Tonooka Hideyuki
Graduate School of Science and Engineering, Ibaraki University, Hitachi 3168511, Japan.
Sensors (Basel). 2024 Feb 23;24(5):1444. doi: 10.3390/s24051444.
Coastal levees play a role in protecting coastal areas from storm surges and high waves, and they provide important input information for inundation damage simulations. However, coastal levee data with uniformity and sufficient accuracy for inundation simulations are not always well developed. Against this background, this study proposed a method to extract coastal levees by inputting high spatial resolution optical satellite image products (RGB images, digital surface models (DSMs), and slope images that can be generated from DSM images), which have high data availability at the locations and times required for simulation, into a deep learning model. The model is based on U-Net, and post-processing for noise removal was introduced to further improve its accuracy. We also proposed a method to calculate levee height using a local maximum filter by giving DSM values to the extracted levee pixels. The validation was conducted in the coastal area of Ibaraki Prefecture in Japan as a test area. The levee mask images for training were manually created by combining these data with satellite images and Google Street View, because the levee GIS data created by the Ibaraki Prefectural Government were incomplete in some parts. First, the deep learning models were compared and evaluated, and it was shown that U-Net was more accurate than Pix2Pix and BBS-Net in identifying levees. Next, three cases of input images were evaluated: (Case 1) RGB image only, (Case 2) RGB and DSM images, and (Case 3) RGB, DSM, and slope images. Case 3 was found to be the most accurate, with an average Matthews correlation coefficient of 0.674. The effectiveness of noise removal post-processing was also demonstrated. In addition, an example of the calculation of levee heights was presented and evaluated for validity. In conclusion, this method was shown to be effective in extracting coastal levees. The evaluation of generalizability and use in actual inundation simulations are future tasks.
沿海堤坝在保护沿海地区免受风暴潮和巨浪侵袭方面发挥着作用，并且为洪水淹没损失模拟提供重要的输入信息。然而，用于淹没模拟的具有一致性和足够精度的沿海堤坝数据并非总是完善的。在此背景下，本研究提出了一种方法，通过将高空间分辨率光学卫星图像产品（RGB图像、数字表面模型（DSM）以及可从DSM图像生成的坡度图像）输入到深度学习模型中来提取沿海堤坝，这些图像产品在模拟所需的位置和时间具有高数据可用性。该模型基于U-Net，并引入了用于去除噪声的后处理以进一步提高其精度。我们还提出了一种方法，通过对提取的堤坝像素赋予DSM值，使用局部最大值滤波器来计算堤坝高度。验证工作在日本茨城县的沿海地区作为试验区进行。由于茨城县政府创建的堤坝GIS数据在某些部分不完整，因此通过将这些数据与卫星图像和谷歌街景相结合，手动创建了用于训练的堤坝掩膜图像。首先，对深度学习模型进行了比较和评估，结果表明U-Net在识别堤坝方面比Pix2Pix和BBS-Net更准确。接下来，对三种输入图像情况进行了评估：（情况1）仅RGB图像，（情况2）RGB和DSM图像，以及（情况3）RGB、DSM和坡度图像。发现情况3最准确，平均马修斯相关系数为0.674。还证明了噪声去除后处理的有效性。此外，给出了一个计算堤坝高度的示例并对其有效性进行了评估。总之，该方法在提取沿海堤坝方面被证明是有效的。评估其通用性以及在实际洪水淹没模拟中的应用是未来的任务。
