Journal article
Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas
Remote Sensing, Vol.11(21), 2575
2019
Abstract
Landslides represent a severe hazard in many areas of the world. Accurate landslide maps are needed to document the occurrence and extent of landslides and to investigate their distribution, types, and the pattern of slope failures. Landslide maps are also crucial for determining landslide susceptibility and risk. Satellite data have been widely used for such investigations-next to data from airborne or unmanned aerial vehicle (UAV)-borne campaigns and Digital Elevation Models (DEMs). We have developed a methodology that incorporates object-based image analysis (OBIA) with three machine learning (ML) methods, namely, the multilayer perceptron neural network (MLP-NN) and random forest (RF), for landslide detection. We identified the optimal scale parameters (SP) and used them for multi-scale segmentation and further analysis. We evaluated the resulting objects using the object pureness index (OPI), object matching index (OMI), and object fitness index (OFI) measures. We then applied two different methods to optimize the landslide detection task: (a) an ensemble method of stacking that combines the different ML methods for improving the performance, and (b) Dempster-Shafer theory (DST), to combine the multi-scale segmentation and classification results. Through the combination of three ML methods and the multi-scale approach, the framework enhanced landslide detection when it was tested for detecting earthquake-triggered landslides in Rasuwa district, Nepal. PlanetScope optical satellite images and a DEM were used, along with the derived landslide conditioning factors. Different accuracy assessment measures were used to compare the results against a field-based landslide inventory. All ML methods yielded the highest overall accuracies ranging from 83.3% to 87.2% when using objects with the optimal SP compared to other SPs. However, applying DST to combine the multi-scale results of each ML method significantly increased the overall accuracies to almost 90%. Overall, the integration of OBIA with ML methods resulted in appropriate landslide detections, but using the optimal SP and ML method is crucial for success.
Details
- Title
- Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas
- Authors
- Sepideh Tavakkoli Piralilou (Author) - University of Salzburg, AustriaHejar Shahabi (Author) - University of Tabriz, IranBen Jarihani (Author) - University of the Sunshine CoastOmid Ghorbanzadeh (Author) - University of Tabriz, IranThomas Blaschke (Author) - University of Salzburg, AustriaKhalil Gholamnia (Author) - University of Tabriz, IranSansar Raj Meena (Author) - University of Tabriz, IranJagannath Aryal (Author) - University of Tasmania
- Publication details
- Remote Sensing, Vol.11(21), 2575; 26
- Publisher
- MDPI AG
- Date published
- 2019
- DOI
- 10.3390/rs11212575
- ISSN
- 2072-4292; 2072-4292
- Copyright note
- Copyright © The Author 2019. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Sustainability Research Cluster
- Language
- English
- Record Identifier
- 99451442402621
- Output Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Environmental Sciences
- Geosciences, Multidisciplinary
- Imaging Science & Photographic Technology
- Remote Sensing
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