Slow-Moving Landslide Hazard Assessment Using LS-Unilab Deep Learning Model with Highlighted InSAR Deformation Signal
Slow-moving landslides are often precursors of catastrophic failure, posing a major threat to human life and property safety. Interferometric synthetic aperture radar (InSAR) has become a crucial tool for investigating slow-moving landslides hazard because of its high-precision detection capability...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/16/24/4641 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850085402596081664 |
|---|---|
| author | Xiangyang Li Peifeng Ma Song Xu Hong Zhang Chao Wang Yukun Fan Yixian Tang |
| author_facet | Xiangyang Li Peifeng Ma Song Xu Hong Zhang Chao Wang Yukun Fan Yixian Tang |
| author_sort | Xiangyang Li |
| collection | DOAJ |
| description | Slow-moving landslides are often precursors of catastrophic failure, posing a major threat to human life and property safety. Interferometric synthetic aperture radar (InSAR) has become a crucial tool for investigating slow-moving landslides hazard because of its high-precision detection capability for slow surface deformation. However, landslides usually occur in alpine canyon areas and vegetation coverage areas where InSAR measurements are still limited by temporal and spatial decorrelation and atmospheric influences. In addition, there are several difficulties in monitoring the multiscale characterization of landslides from the InSAR results. To address this issue, this paper proposes a novel method for slow-moving landslide hazard assessment in low-coherence regions. A window-based atmosphere correction method is designed to highlight the surface deformation signals of InSAR results in low-coherence regions and reduce false alarms in landslide hazard assessment. Then, the deformation annual velocity rate map, coherence map and DEM are used to construct the InSAR sample set. A landslide hazard assessment model named Landslide-SE-Unilab is subsequently proposed. The global–local relationship aggregation structure is designed to capture the spatial relationship between local pixel-level deformation features and global landslides, which can reduce the number of missed assessments and false assessments of small-scale landslides. Additionally, a squeeze-and-excitation network is embedded to adjust the weight relationship between the features of each channel in order to enhance the performance of network evaluation. The method was evaluated in Kangding city and the Jinsha River Valley in the Hengduan Mountains, where a total of 778 potential landslides with slow deformation were identified. The effectiveness and accuracy of this approach for low-coherence landslide hazard assessment are demonstrated through comparisons with optical images and previous research findings, as well as evaluations via time-series deformation results. |
| format | Article |
| id | doaj-art-45500062a9d04d1b85b59f46621e1b7e |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-45500062a9d04d1b85b59f46621e1b7e2025-08-20T02:43:43ZengMDPI AGRemote Sensing2072-42922024-12-011624464110.3390/rs16244641Slow-Moving Landslide Hazard Assessment Using LS-Unilab Deep Learning Model with Highlighted InSAR Deformation SignalXiangyang Li0Peifeng Ma1Song Xu2Hong Zhang3Chao Wang4Yukun Fan5Yixian Tang6Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaInstitute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, ChinaGuangdong GDH Pearl River Delta Water Supply Co., Ltd., Guangzhou 511458, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaSlow-moving landslides are often precursors of catastrophic failure, posing a major threat to human life and property safety. Interferometric synthetic aperture radar (InSAR) has become a crucial tool for investigating slow-moving landslides hazard because of its high-precision detection capability for slow surface deformation. However, landslides usually occur in alpine canyon areas and vegetation coverage areas where InSAR measurements are still limited by temporal and spatial decorrelation and atmospheric influences. In addition, there are several difficulties in monitoring the multiscale characterization of landslides from the InSAR results. To address this issue, this paper proposes a novel method for slow-moving landslide hazard assessment in low-coherence regions. A window-based atmosphere correction method is designed to highlight the surface deformation signals of InSAR results in low-coherence regions and reduce false alarms in landslide hazard assessment. Then, the deformation annual velocity rate map, coherence map and DEM are used to construct the InSAR sample set. A landslide hazard assessment model named Landslide-SE-Unilab is subsequently proposed. The global–local relationship aggregation structure is designed to capture the spatial relationship between local pixel-level deformation features and global landslides, which can reduce the number of missed assessments and false assessments of small-scale landslides. Additionally, a squeeze-and-excitation network is embedded to adjust the weight relationship between the features of each channel in order to enhance the performance of network evaluation. The method was evaluated in Kangding city and the Jinsha River Valley in the Hengduan Mountains, where a total of 778 potential landslides with slow deformation were identified. The effectiveness and accuracy of this approach for low-coherence landslide hazard assessment are demonstrated through comparisons with optical images and previous research findings, as well as evaluations via time-series deformation results.https://www.mdpi.com/2072-4292/16/24/4641slow-moving landslidehazard assessmentInSARLS-Unilab |
| spellingShingle | Xiangyang Li Peifeng Ma Song Xu Hong Zhang Chao Wang Yukun Fan Yixian Tang Slow-Moving Landslide Hazard Assessment Using LS-Unilab Deep Learning Model with Highlighted InSAR Deformation Signal Remote Sensing slow-moving landslide hazard assessment InSAR LS-Unilab |
| title | Slow-Moving Landslide Hazard Assessment Using LS-Unilab Deep Learning Model with Highlighted InSAR Deformation Signal |
| title_full | Slow-Moving Landslide Hazard Assessment Using LS-Unilab Deep Learning Model with Highlighted InSAR Deformation Signal |
| title_fullStr | Slow-Moving Landslide Hazard Assessment Using LS-Unilab Deep Learning Model with Highlighted InSAR Deformation Signal |
| title_full_unstemmed | Slow-Moving Landslide Hazard Assessment Using LS-Unilab Deep Learning Model with Highlighted InSAR Deformation Signal |
| title_short | Slow-Moving Landslide Hazard Assessment Using LS-Unilab Deep Learning Model with Highlighted InSAR Deformation Signal |
| title_sort | slow moving landslide hazard assessment using ls unilab deep learning model with highlighted insar deformation signal |
| topic | slow-moving landslide hazard assessment InSAR LS-Unilab |
| url | https://www.mdpi.com/2072-4292/16/24/4641 |
| work_keys_str_mv | AT xiangyangli slowmovinglandslidehazardassessmentusinglsunilabdeeplearningmodelwithhighlightedinsardeformationsignal AT peifengma slowmovinglandslidehazardassessmentusinglsunilabdeeplearningmodelwithhighlightedinsardeformationsignal AT songxu slowmovinglandslidehazardassessmentusinglsunilabdeeplearningmodelwithhighlightedinsardeformationsignal AT hongzhang slowmovinglandslidehazardassessmentusinglsunilabdeeplearningmodelwithhighlightedinsardeformationsignal AT chaowang slowmovinglandslidehazardassessmentusinglsunilabdeeplearningmodelwithhighlightedinsardeformationsignal AT yukunfan slowmovinglandslidehazardassessmentusinglsunilabdeeplearningmodelwithhighlightedinsardeformationsignal AT yixiantang slowmovinglandslidehazardassessmentusinglsunilabdeeplearningmodelwithhighlightedinsardeformationsignal |