Susceptibility-Guided Landslide Detection Using Fully Convolutional Neural Network
Automatic landslide detection based on very high spatial resolution remote sensing images is crucial for disaster prevention and mitigation applications. With the rapid development of deep-learning techniques, state-of-the-art semantic segmentation methods based on fully convolutional network (FCNN)...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10003643/ |
| _version_ | 1797957249618935808 |
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| author | Yangyang Chen Dongping Ming Junchuan Yu Lu Xu Yanni Ma Yan Li Xiao Ling Yueqin Zhu |
| author_facet | Yangyang Chen Dongping Ming Junchuan Yu Lu Xu Yanni Ma Yan Li Xiao Ling Yueqin Zhu |
| author_sort | Yangyang Chen |
| collection | DOAJ |
| description | Automatic landslide detection based on very high spatial resolution remote sensing images is crucial for disaster prevention and mitigation applications. With the rapid development of deep-learning techniques, state-of-the-art semantic segmentation methods based on fully convolutional network (FCNN) have achieved outstanding performance in the landslide detection task. However, most of the existing articles only utilize visual features. Even if the advanced FCNN models are applied, there is still a certain amount of falsely detected and miss detected landslides. In this article, we innovatively introduce landslide susceptibility as prior knowledge and propose an innovative susceptibility-guided landslide detection method based on FCNN (SG-FCNN) to detect landslides from single temporal images. In addition, an unsupervised change detection method based on the mean changing magnitude of objects (MCMO) is further proposed and integrated with the SG-FCNN to detect newly occurred landslides from bitemporal images. The effectiveness of the proposed SG-FCNN and MCMO has been tested in Lantau Island, Hong Kong. The experimental results show that the SG-FCNN can significantly reduce the amount of falsely detected and miss detected landslides compared with the FCNN. It can conclude that applying landslide susceptibility as prior knowledge is much more effective than using visual features only, which introduces a new methodology of landslide detection and lifts the detection performance to a new level. |
| first_indexed | 2024-04-11T00:02:29Z |
| format | Article |
| id | doaj.art-a72b7042b7cf434cad2dc28fd651bebd |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-11T00:02:29Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-a72b7042b7cf434cad2dc28fd651bebd2023-01-10T00:00:12ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-0116998101810.1109/JSTARS.2022.323304310003643Susceptibility-Guided Landslide Detection Using Fully Convolutional Neural NetworkYangyang Chen0https://orcid.org/0000-0001-9349-4246Dongping Ming1https://orcid.org/0000-0002-3422-7399Junchuan Yu2Lu Xu3Yanni Ma4Yan Li5Xiao Ling6https://orcid.org/0000-0002-5903-1210Yueqin Zhu7China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing, ChinaSchool of Information Engineering, China University of Geosciences, Beijing, ChinaChina Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing, ChinaSchool of Information Engineering, China University of Geosciences, Beijing, ChinaChina Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing, ChinaSchool of Information Engineering, China University of Geosciences, Beijing, ChinaSchool of Information Engineering, China University of Geosciences, Beijing, ChinaNational Institute of Natural Hazards, Ministry of Emergency Management, Beijing, ChinaAutomatic landslide detection based on very high spatial resolution remote sensing images is crucial for disaster prevention and mitigation applications. With the rapid development of deep-learning techniques, state-of-the-art semantic segmentation methods based on fully convolutional network (FCNN) have achieved outstanding performance in the landslide detection task. However, most of the existing articles only utilize visual features. Even if the advanced FCNN models are applied, there is still a certain amount of falsely detected and miss detected landslides. In this article, we innovatively introduce landslide susceptibility as prior knowledge and propose an innovative susceptibility-guided landslide detection method based on FCNN (SG-FCNN) to detect landslides from single temporal images. In addition, an unsupervised change detection method based on the mean changing magnitude of objects (MCMO) is further proposed and integrated with the SG-FCNN to detect newly occurred landslides from bitemporal images. The effectiveness of the proposed SG-FCNN and MCMO has been tested in Lantau Island, Hong Kong. The experimental results show that the SG-FCNN can significantly reduce the amount of falsely detected and miss detected landslides compared with the FCNN. It can conclude that applying landslide susceptibility as prior knowledge is much more effective than using visual features only, which introduces a new methodology of landslide detection and lifts the detection performance to a new level.https://ieeexplore.ieee.org/document/10003643/Convolutional neural network (CNN)landslide detectionlandslide susceptibility mappingLantau Islandremote sensing |
| spellingShingle | Yangyang Chen Dongping Ming Junchuan Yu Lu Xu Yanni Ma Yan Li Xiao Ling Yueqin Zhu Susceptibility-Guided Landslide Detection Using Fully Convolutional Neural Network IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Convolutional neural network (CNN) landslide detection landslide susceptibility mapping Lantau Island remote sensing |
| title | Susceptibility-Guided Landslide Detection Using Fully Convolutional Neural Network |
| title_full | Susceptibility-Guided Landslide Detection Using Fully Convolutional Neural Network |
| title_fullStr | Susceptibility-Guided Landslide Detection Using Fully Convolutional Neural Network |
| title_full_unstemmed | Susceptibility-Guided Landslide Detection Using Fully Convolutional Neural Network |
| title_short | Susceptibility-Guided Landslide Detection Using Fully Convolutional Neural Network |
| title_sort | susceptibility guided landslide detection using fully convolutional neural network |
| topic | Convolutional neural network (CNN) landslide detection landslide susceptibility mapping Lantau Island remote sensing |
| url | https://ieeexplore.ieee.org/document/10003643/ |
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