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Sensors 2018, 18(3), 821; https://doi.org/10.3390/s18030821

Automated Landslides Detection for Mountain Cities Using Multi-Temporal Remote Sensing Imagery

National Key Laboratory of Science & Technology on Multi-Spectral Information Processing, Huazhong University of Science and Technology, 430074 Wuhan, China
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Received: 26 January 2018 / Revised: 5 March 2018 / Accepted: 6 March 2018 / Published: 9 March 2018
(This article belongs to the Special Issue Advances in Remote Sensing of Land-Cover and Land-Use Changes)
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Abstract

Landslides that take place in mountain cities tend to cause huge casualties and economic losses, and a precise survey of landslide areas is a critical task for disaster emergency. However, because of the complicated appearance of the nature, it is difficult to find a spatial regularity that only relates to landslides, thus landslides detection based on only spatial information or artificial features usually performs poorly. In this paper, an automated landslides detection approach that is aiming at mountain cities has been proposed based on pre- and post-event remote sensing images, it mainly utilizes the knowledge of landslide-related surface covering changes, and makes full use of the temporal and spatial information. A change detection method using Deep Convolution Neural Network (DCNN) was introduced to extract the areas where drastic alterations have taken place; then, focusing on the changed areas, the Spatial Temporal Context Learning (STCL) was conducted to identify the landslides areas; finally, we use slope degree which is derived from digital elevation model (DEM) to make the result more reliable, and the change of DEM is used for making the detected areas more complete. The approach was applied to detecting the landslides in Shenzhen, Zhouqu County and Beichuan County in China, and a quantitative accuracy assessment has been taken. The assessment indicates that this approach can guarantee less commission error of landslide areal extent which is below 17.6% and achieves a quality percentage above 61.1%, and for landslide areas, the detection percentage is also competitive, the experimental results proves the feasibility and accuracy of the proposed approach for the detection landslides in mountain cities. View Full-Text
Keywords: landslides detection; remote sensing images; change detection; Deep Convolution Neural Network; Spatial Temporal Context Learning landslides detection; remote sensing images; change detection; Deep Convolution Neural Network; Spatial Temporal Context Learning
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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. (CC BY 4.0).
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Chen, Z.; Zhang, Y.; Ouyang, C.; Zhang, F.; Ma, J. Automated Landslides Detection for Mountain Cities Using Multi-Temporal Remote Sensing Imagery. Sensors 2018, 18, 821.

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