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

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 consumin...

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Main Authors: Mezaal, Al-Karawi Mustafa Ridha, Pradhan, Biswajeet, Sameen, Maher Ibrahim, Mohd Shafri, Helmi Zulhaidi, Md Yusoff, Zainuddin
Format: Article
Language:English
Published: MDPI 2017
Online Access:http://psasir.upm.edu.my/id/eprint/64636/1/64636.pdf
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spelling oai:psasir.upm.edu.my:64636 http://psasir.upm.edu.my/id/eprint/64636/ Optimized neural architecture for automatic landslide detection from high-resolution airborne laser scanning data Mezaal, Al-Karawi Mustafa Ridha Pradhan, Biswajeet Sameen, Maher Ibrahim Mohd Shafri, Helmi Zulhaidi Md Yusoff, Zainuddin 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. MDPI 2017 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/64636/1/64636.pdf 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 http://www.mdpi.com/2076-3417/7/7/730 10.3390/app7070730
institution UPM IR
collection UPM IR
language English
description 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.
format Article
author Mezaal, Al-Karawi Mustafa Ridha
Pradhan, Biswajeet
Sameen, Maher Ibrahim
Mohd Shafri, Helmi Zulhaidi
Md Yusoff, Zainuddin
spellingShingle Mezaal, Al-Karawi Mustafa Ridha
Pradhan, Biswajeet
Sameen, Maher Ibrahim
Mohd Shafri, Helmi Zulhaidi
Md Yusoff, Zainuddin
Optimized neural architecture for automatic landslide detection from high-resolution airborne laser scanning data
author_facet Mezaal, Al-Karawi Mustafa Ridha
Pradhan, Biswajeet
Sameen, Maher Ibrahim
Mohd Shafri, Helmi Zulhaidi
Md Yusoff, Zainuddin
author_sort Mezaal, Al-Karawi Mustafa Ridha
title Optimized neural architecture for automatic landslide detection from high-resolution airborne laser scanning data
title_short Optimized neural architecture for automatic landslide detection from high-resolution airborne laser scanning data
title_full Optimized neural architecture for automatic landslide detection from high-resolution airborne laser scanning data
title_fullStr Optimized neural architecture for automatic landslide detection from high-resolution airborne laser scanning data
title_full_unstemmed Optimized neural architecture for automatic landslide detection from high-resolution airborne laser scanning data
title_sort optimized neural architecture for automatic landslide detection from high-resolution airborne laser scanning data
publisher MDPI
publishDate 2017
url http://psasir.upm.edu.my/id/eprint/64636/1/64636.pdf
_version_ 1782756737375272960
score 12.935284

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