Correlation-based feature optimization and object-based approach for distinguishing shallow and deep-seated landslides using high resolution airborne laser scanning data

9th IGRSM International Conference and Exhibition on Geospatial & Remote Sensing

地球科学;计算机科学

Rmezaal, M.^1 ; Pradhan, B.^1,2

Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Selangor

43400, Malaysia^1

School of Systems, Management and Leadership, Faculty of Engineering and IT, University of Technology Sydney, 81 Broadway, Ultimo

NSW

2007, Australia^2

关键词: Airborne Laser scanning;    Correlation based feature selections;    Deep seated landslide;    Feature selection methods;    Landslide identification;    Landslide susceptibility;    Model transferabilities;    Optimization techniques;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/169/1/012048/pdf
DOI  :  10.1088/1755-1315/169/1/012048

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Landslides post great threats to many regions globally, particularly in densely vegetated areas where they are hard to identify. Thus, in order to address this issue, precise inventory mapping methods are required in order to gauge landslide susceptibility in regions, as well as hazards and risk. Obstacles in the development of such mapping methods, however, are optimization techniques to employ, feature selection methods, as well as the development of model transferability. The present study seeks to utilize correlation-based feature selection and object-based approach in conjunction with LiDAR data, whereby LiDAR-DEM derived digital elevation alongside high-resolution orthophotos are employed in tandem. Next, fuzzy-based segmentation parameter optimizer was employed in order to optimize segmentation parameters. Next, support vector machine was employed in order to assess the effectiveness of the proposed method, with results illustrating the algorithm's robustness with regards to landslide identification. The results of transferability also demonstrated the ease of use for the method, as well as its accuracy and capability to identify landslides as either shallow or deep-seated. To summarize, the study proposes that the developed methods are greatly effective in landslide detection, especially in tropical regions such as in Malaysia.