TY - JOUR
T1 - Noncontact detection of earthquake-induced landslides by an enhanced image binarization method incorporating with Monte-Carlo simulation
AU - Han, Zheng
AU - Li, Yange
AU - Du, Yinfei
AU - Wang, Weidong
AU - Chen, Guangqi
N1 - Funding Information:
This study was financially supported by the National Key R&D Program of China (Grant No. 2017YFB1201204, Z. Han); the National Natural Science Foundation of China (Grant No. 41702310, Z. Han); the Foundation of State Key Laboratory of Geo-Hazard Prevention and Geo-Environment Protection (Grant No. SKLGP2017K014, Z. Han); and the Natural Science Foundation of Hunan Province, China (Grant No. 2018JJ3644, Z. Han). These financial supports are gratefully acknowledged. The authors also extend their gratitude to editor-in-chief Ramesh Singh, and two nominated reviewers for their insightful comments.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Detecting landslides using remote sensing images involves converting gray level images into binary images. Given the complex background and non-uniform illumination in the regional remote sensing image, the commonly-used global thresholding methods are limited to interpret some landslides those hidden in the shadow. In this paper, we report on an enhanced image thresholding method for the noncontact detection of earthquake-induced landslides. The proposed method incorporates Monte-Carlo simulation into the local thresholding method, and essential issues, regarding complex illumination condition and uncertainties of determining block size in local thresholding method, are addressed. To better separate landslide candidate objects from the background, we incorporate Digital Surface Model (DSM) into the binary image, such that the interferences by built-up areas, terraces, and rivers can be significantly reduced considering the indicator of slope gradient. The described method has been tested using two benchmark tests, showing that the proposed method performs well dealing with the complex background and illumination condition. As a case study, we use the proposed method to detect landslides in a remote sensing image near Beichuan area after the 2008 Ms8.0 Wenchuan earthquake. Results demonstrate that the presented method interprets landslides resembling those of manually visual delineation.
AB - Detecting landslides using remote sensing images involves converting gray level images into binary images. Given the complex background and non-uniform illumination in the regional remote sensing image, the commonly-used global thresholding methods are limited to interpret some landslides those hidden in the shadow. In this paper, we report on an enhanced image thresholding method for the noncontact detection of earthquake-induced landslides. The proposed method incorporates Monte-Carlo simulation into the local thresholding method, and essential issues, regarding complex illumination condition and uncertainties of determining block size in local thresholding method, are addressed. To better separate landslide candidate objects from the background, we incorporate Digital Surface Model (DSM) into the binary image, such that the interferences by built-up areas, terraces, and rivers can be significantly reduced considering the indicator of slope gradient. The described method has been tested using two benchmark tests, showing that the proposed method performs well dealing with the complex background and illumination condition. As a case study, we use the proposed method to detect landslides in a remote sensing image near Beichuan area after the 2008 Ms8.0 Wenchuan earthquake. Results demonstrate that the presented method interprets landslides resembling those of manually visual delineation.
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U2 - 10.1080/19475705.2018.1520745
DO - 10.1080/19475705.2018.1520745
M3 - Article
AN - SCOPUS:85068741130
VL - 10
SP - 219
EP - 241
JO - Geomatics, Natural Hazards and Risk
JF - Geomatics, Natural Hazards and Risk
SN - 1947-5705
IS - 1
ER -