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Optimized neural architecture for automatic landslide detection from high-resolution airborne laser scanning data


Citation

Mezaal, Al-Karawi Mustafa Ridha and Pradhan, Biswajeet and Sameen, Maher Ibrahim and Mohd Shafri, Helmi Zulhaidi and Md Yusoff, Zainuddin (2017) Optimized neural architecture for automatic landslide detection from high-resolution airborne laser scanning data. Applied Sciences, 7 (7). art. no. 730. pp. 1-20. ISSN 2076-3417

Abstract

An accurate inventory map is a prerequisite for the analysis of landslide susceptibility, hazard, and risk. Field survey, optical remote sensing, and synthetic aperture radar techniques are traditional techniques for landslide detection in tropical regions. However, such techniques are time consuming and costly. In addition, the dense vegetation of tropical forests complicates the generation of an accurate landslide inventory map for these regions. Given its ability to penetrate vegetation cover, high-resolution airborne light detection and ranging (LiDAR) has been used to generate accurate landslide maps. This study proposes the use of recurrent neural networks (RNN) and multi-layer perceptron neural networks (MLP-NN) in landscape detection. These efficient neural architectures require little or no prior knowledge compared with traditional classification methods. The proposed methods were tested in the Cameron Highlands, Malaysia. Segmentation parameters and feature selection were respectively optimized using a supervised approach and correlation-based feature selection. The hyper-parameters of network architecture were defined based on a systematic grid search. The accuracies of the RNN and MLP-NN models in the analysis area were 83.33% and 78.38%, respectively. The accuracies of the RNN and MLP-NN models in the test area were 81.11%, and 74.56%, respectively. These results indicated that the proposed models with optimized hyper-parameters produced the most accurate classification results. LiDAR-derived data, orthophotos, and textural features significantly affected the classification results. Therefore, the results indicated that the proposed methods have the potential to produce accurate and appropriate landslide inventory in tropical regions such as Malaysia.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.3390/app7070730
Publisher: MDPI
Keywords: Landslide detection; LiDAR; Recurrent neural networks (RNN); Multi‐layer perceptron neural networks (MLP‐NN); GIS; Remote sensing
Depositing User: Nabilah Mustapa
Date Deposited: 13 Aug 2018 03:16
Last Modified: 13 Aug 2018 03:16
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.3390/app7070730
URI: http://psasir.upm.edu.my/id/eprint/64636
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