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Journal of Applied Remote Sensing

Automatic identification of shallow landslides based on Worldview2 remote sensing images
Author(s): Hai-Rong Ma; Xinwen Cheng; Lianjun Chen; Haitao Zhang; Hongwei Xiong
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Paper 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.

Paper Details

Date Published: 4 February 2016
PDF: 12 pages
J. Appl. Rem. Sens. 10(1) 016008 doi: 10.1117/1.JRS.10.016008
Published in: Journal of Applied Remote Sensing Volume 10, Issue 1
Show Author Affiliations
Hai-Rong Ma, China Univ. of Geosciences (China)
Xinwen Cheng, China Univ. of Geosciences (China)
Wuhan Unv. of Engineering Science (China)
Lianjun Chen, China Univ. of Geosciences (China)
Haitao Zhang, China Univ. of Geosciences (China)
Hongwei Xiong, China Univ. of Geosciences (China)

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