Identification of Landslides in Mountainous Area with the Combination of SBAS-InSAR and Yolo Model
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. How...
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| Format: | Article |
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MDPI AG
2022-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/16/6235 |
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| author | Haojia Guo Bangjin Yi Qianxiang Yao Peng Gao Hui Li Jixing Sun Cheng Zhong |
| author_facet | Haojia Guo Bangjin Yi Qianxiang Yao Peng Gao Hui Li Jixing Sun Cheng Zhong |
| author_sort | Haojia Guo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T03:51:07Z |
| format | Article |
| id | doaj.art-0ace8d3d9e41455fbe09cc46326ea5ab |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T03:51:07Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0ace8d3d9e41455fbe09cc46326ea5ab2023-12-03T14:27:25ZengMDPI AGSensors1424-82202022-08-012216623510.3390/s22166235Identification of Landslides in Mountainous Area with the Combination of SBAS-InSAR and Yolo ModelHaojia Guo0Bangjin Yi1Qianxiang Yao2Peng Gao3Hui Li4Jixing Sun5Cheng Zhong6Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, ChinaYunnan Institute of Geological Science, Kunming 650051, ChinaBadong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, ChinaDepartment of Earth and Ocean Sciences, University of North Carolina, Wilmington, NC 28403, USASchool of Earth Sciences, China University of Geosciences, Wuhan 430074, ChinaYunnan Institute of Geological Science, Kunming 650051, ChinaBadong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, ChinaLandslides 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.https://www.mdpi.com/1424-8220/22/16/6235landslidesSentinel-1InSARdeep learninghigh resolution image |
| spellingShingle | Haojia Guo Bangjin Yi Qianxiang Yao Peng Gao Hui Li Jixing Sun Cheng Zhong Identification of Landslides in Mountainous Area with the Combination of SBAS-InSAR and Yolo Model Sensors landslides Sentinel-1 InSAR deep learning high resolution image |
| title | Identification of Landslides in Mountainous Area with the Combination of SBAS-InSAR and Yolo Model |
| title_full | Identification of Landslides in Mountainous Area with the Combination of SBAS-InSAR and Yolo Model |
| title_fullStr | Identification of Landslides in Mountainous Area with the Combination of SBAS-InSAR and Yolo Model |
| title_full_unstemmed | Identification of Landslides in Mountainous Area with the Combination of SBAS-InSAR and Yolo Model |
| title_short | Identification of Landslides in Mountainous Area with the Combination of SBAS-InSAR and Yolo Model |
| title_sort | identification of landslides in mountainous area with the combination of sbas insar and yolo model |
| topic | landslides Sentinel-1 InSAR deep learning high resolution image |
| url | https://www.mdpi.com/1424-8220/22/16/6235 |
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