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Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
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Sensors and Materials, Volume 32, Number 11(4) (2020)
Copyright(C) MYU K.K.
pp. 4019-4036
S&M2391 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2020.3131
Published in advance: November 20, 2020
Published: November 30, 2020

Automatic Unsupervised Landslide Detection Method Based on Single High-resolution Optical Image for Emergency Response [PDF]

Xi Zhai, Wanzeng Liu, Chuan Yin, Yunlu Peng, Yong Zhao, Ying Yang, Xiuli Zhu, Ran Li, and Tingting Zhao

(Received September 29, 2020; Accepted November 17, 2020)

Keywords: urban landslide detection, high-resolution image, visual salience, reflective characteristics, morphological processing

Urban safety in mountainous areas is continuously seriously threatened by landslide disasters. Recently, many remote-sensing-based methods have been developed for landslide detection. However, many existing methods rely on multitemporal/multisource data, which require tedious data collection work and limit their practical capacity in real-time emergencies. Therefore, in this paper, we propose a novel unsupervised single-image-based landslide detection (USILD) method to automatically and quickly locate landslides and evaluate landslide risks, which can provide timely data for urban landslide responses. This method is designed to take full advantage of the visual salience and reflectance characteristics of landslides to produce a landslide risk map. Morphological processing is used to refine the final maps. The method is implemented and applied to the recent Ludian landslide event, in which hundreds of landslides occurred, on August 3, 2014. High-resolution satellite and aerial images obtained with sensor technology provided suitable experimental materials for this study. The experimental results show that our method can achieve higher accuracy and more automatic processing than other methods such as change detection using image differencing (CDD), change detection using ratio (CDR), k-means, and support vector machine (SVM). Moreover, the method requires no training samples and has a lower computational cost than supervised learning algorithms. Given the high detection accuracy and simple workflow, the proposed method is very promising for practical application in landslide emergency responses.

Corresponding author: Wanzeng Liu, Yong Zhao


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Cite this article
Xi Zhai, Wanzeng Liu, Chuan Yin, Yunlu Peng, Yong Zhao, Ying Yang, Xiuli Zhu, Ran Li, and Tingting Zhao, Automatic Unsupervised Landslide Detection Method Based on Single High-resolution Optical Image for Emergency Response, Sens. Mater., Vol. 32, No. 11, 2020, p. 4019-4036.



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