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

Publication Type:
Conference Proceeding
IOP Conference Series: Earth and Environmental Science, 2018, 169 (1)
Issue Date:
Full metadata record
© Published under licence by IOP Publishing Ltd. 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.
Please use this identifier to cite or link to this item: