Remote Sensing Applications and Decision Support

Automatic identification of shallow landslides based on Worldview2 remote sensing images

[+] Author Affiliations
Hai-Rong Ma, Lianjun Chen, Haitao Zhang, Hongwei Xiong

China University of Geosciences, Faculty of Information Engineering, Department of Surveying and Mapping Engineering, Room 303, Xingong Building, 388 Lumo Road, Hongshan District, Wuhan 430074, Hubei, China

Xinwen Cheng

China University of Geosciences, Faculty of Information Engineering, Department of Surveying and Mapping Engineering, Room 303, Xingong Building, 388 Lumo Road, Hongshan District, Wuhan 430074, Hubei, China

Wuhan University of Engineering Science, Room 405, Administrative Building, No. 8, Xiongtingbi Road, Jiangxia District, Wuhan 430200, Hubei, China

J. Appl. Remote Sens. 10(1), 016008 (Feb 04, 2016). doi:10.1117/1.JRS.10.016008
History: Received July 21, 2015; Accepted January 8, 2016
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Abstract.  Automatic identification of landslides based on remote sensing images is important for investigating disasters and producing hazard maps. We propose a method to detect shallow landslides automatically using Wordview2 images. Features such as high soil brightness and low vegetation coverage can help identify shallow landslides on remote sensing images. Therefore, soil brightness and vegetation index were chosen as indexes for landslide remote sensing. The back scarp of a landslide can form dark shadow areas on the landslide mass, affecting the accuracy of landslide extraction. To eliminate this effect, the shadow index was chosen as an index. The first principal component (PC1) contained >90% of the image information; therefore, this was also selected as an index. The four selected indexes were used to synthesize a new image wherein information on shallow landslides was enhanced, while other background information was suppressed. Then, PC1 was extracted from the new synthetic image, and an automatic threshold segmentation algorithm was used for segmenting the image to obtain similar landslide areas. Based on landslide features such as slope, shape, and area, nonlandslide areas were eliminated. Finally, four experimental sites were used to verify the feasibility of the developed method.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Hai-Rong Ma ; Xinwen Cheng ; Lianjun Chen ; Haitao Zhang and Hongwei Xiong
"Automatic identification of shallow landslides based on Worldview2 remote sensing images", J. Appl. Remote Sens. 10(1), 016008 (Feb 04, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.016008


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