Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China.
Three Gorges Research Center for Geo-Hazard, Ministry of Education, China University of Geosciences, Wuhan 430074, China.
Sensors (Basel). 2022 Aug 19;22(16):6235. doi: 10.3390/s22166235.
Landslides have been frequently occurring in the high mountainous areas in China and poses serious threats to peoples' lives and property, economic development, and national security. Detecting and monitoring quiescent or active landslides is important for predicting risks and mitigating losses. However, traditional ground survey methods, such as field investigation, GNSS, and total stations, are only suitable for field investigation at a specific site rather than identifying landslides over a large area, as they are expensive, time-consuming, and laborious. In this study, the feasibility of using SBAS-InSAR to detect landslides in the high mountainous areas along the Yunnan Myanmar border was tested first, with fifty-four IW mode Sentinel-1A ascending scenes from 12 January 2019 to 8 December 2020. Next, the Yolo deep-learning model with Gaofen-2 images captured on 5 December 2020 was tested. Finally, the two techniques were combined to achieve better performance, given each of them has intrinsic limitations on landslide detection. The experiment indicated that the combination could improve the match rate between detection results and references, which implied that the performance of landslide detection can be improved with the fusion of time series SAR images and optical images.
在中国的高山地区，滑坡经常发生，对人民的生命财产、经济发展和国家安全构成严重威胁。检测和监测静止或活动的滑坡对于预测风险和减轻损失非常重要。然而，传统的地面调查方法，如实地调查、GNSS 和全站仪，仅适用于特定地点的现场调查，而不适用于大面积的滑坡识别，因为这些方法昂贵、耗时且费力。在本研究中，首先测试了使用 SBAS-InSAR 检测云南缅甸边境高山区滑坡的可行性，使用了 2019 年 1 月 12 日至 2020 年 12 月 8 日的 54 个 IW 模式 Sentinel-1A 升轨场景。接下来，测试了 2020 年 12 月 5 日拍摄的高分二号图像的 Yolo 深度学习模型。最后，结合这两种技术以获得更好的性能，因为它们在滑坡检测方面都存在固有局限性。实验表明，组合可以提高检测结果与参考之间的匹配率，这意味着通过融合时间序列 SAR 图像和光学图像可以提高滑坡检测的性能。
