Automatic identification of shallow landslides based on Worldview2 remote sensing images

Hai-Rong Ma; Xinwen Cheng; Lianjun Chen; Haitao Zhang; Hongwei Xiong

doi:10.1117/1.JRS.10.016008

4 February 2016 Automatic identification of shallow landslides based on Worldview2 remote sensing images

Hai-Rong Ma, Xinwen Cheng, Lianjun Chen, Haitao Zhang, Hongwei Xiong

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 1, 016008 (February 2016). https://doi.org/10.1117/1.JRS.10.016008

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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Hai-Rong Ma, Xinwen Cheng, Lianjun Chen, Haitao Zhang, and Hongwei Xiong "Automatic identification of shallow landslides based on Worldview2 remote sensing images," Journal of Applied Remote Sensing 10(1), 016008 (4 February 2016). https://doi.org/10.1117/1.JRS.10.016008

Published: 4 February 2016

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