Automatic Mapping of Potential Landslides Using Satellite Multitemporal Interferometry
Automatic Mapping of Potential Landslides Using Satellite Multitemporal Interferometry
复制标题
DOI：
10.3390/rs15204951
复制
发表时间：
2023-10
期刊：
影响因子：
--
通讯作者：
Yi Zhang;Yuanxi Li;Xing-min Meng;Wangcai Liu;Ai-Jun Wang;Yiwen Liang;Xiaojun Su;R. Zeng
中科院分区：
文献类型：
--
作者：
Yi Zhang;Yuanxi Li;Xing-min Meng;Wangcai Liu;Ai-Jun Wang;Yiwen Liang;Xiaojun Su;R. Zeng
绘制潜在滑坡图对于减轻和预防滑坡灾害以及了解山区景观演变至关重要。然而，现有的方法来映射和演示潜在的山体滑坡在山区是具有挑战性的使用和效率低下。因此，在这里，我们提出了一种方法，使用热点分析和卷积神经网络映射潜在的山体滑坡在区域尺度上的基础上，地面变形检测多时相干涉合成孔径雷达。通过处理从哨兵-1A号卫星的下降和上升轨道获得的76幅图像，探测到了地面变形。利用热点分析方法对研究区606个地表变形较大的边坡进行了自动检测，提取准确率和缺失率分别为71.02%和7.89%。随后，基于高变形区域和潜在的滑坡条件因素，我们将卷积神经网络的性能与随机森林算法进行了比较，并构建了一个分类模型，其曲线下面积（AUC），准确率，召回率和精度分别为0.75，0.75，0.82和0.75。我们的方法支持干涉合成孔径雷达（干涉合成孔径雷达）的能力，以绘制潜在的滑坡区域，并提供滑坡风险管理的科学基础。它还可以在短时间内和极端危险的条件下准确有效地识别潜在的山体滑坡。
Mapping potential landslides is crucial to mitigating and preventing landslide disasters and understanding mountain landscape evolution. However, the existing methods to map and demonstrate potential landslides in mountainous regions are challenging to use and inefficient. Therefore, herein, we propose a method using hot spot analysis and convolutional neural networks to map potential landslides in mountainous areas at a regional scale based on ground deformation detection using multitemporal interferometry synthetic aperture radar. Ground deformations were detected by processing 76 images acquired from the descending and ascending orbits of the Sentinel-1A satellite. In total, 606 slopes with large ground deformations were automatically detected using hot spot analysis in the study area, and the extraction accuracy rate and the missing rate are 71.02% and 7.89%, respectively. Subsequently, based on the high-deformation areas and potential landslide conditioning factors, we compared the performance of convolutional neural networks with the random forest algorithm and constructed a classification model with the area under the curve (AUC), accuracy, recall, and precision for testing being 0.75, 0.75, 0.82, and 0.75, respectively. Our approach underpins the ability of interferometric synthetic aperture radar (InSAR) to map potential landslides regionally and provide a scientific foundation for landslide risk management. It also enables an accurate and efficient identification of potential landslides within a short period and under extremely hazardous conditions.