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Journal of Applied Remote Sensing

Large-scale landslide detection for practical use based on image saliency
Author(s): Bo Yu; Fang Chen
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Paper Abstract

This paper presents a practical method for landslide detection, using single-temporal Landsat8 image at a spatial resolution of 30 m, which is publicly available. The method introduces the concept “saliency” to express potential landslide regions in the image. Based on the saliency calculations, landslides are further extracted by object-based contours using morphological operations. The experimental area covers 2 deg×2 deg, which is more practical for most cases, and the performance validates the efficiency and robustness of this method in practical applications. The overall accuracy in terms of landslide/background classification reaches 99.87%, indicating the proposed method is viable. Even though the commission error and omission error of the detected landslides are both above 50%, the proposed method is able to remove ∼99.9% background, thus reducing manual interpretation to a large extent. Moreover, since it does not need training data or many experienced parameters, it is much easier to be applied to other cases. This paper moves forward a step in landslide detection toward practical applications, such as emergency response.

Paper Details

Date Published: 5 November 2016
PDF: 18 pages
J. Appl. Rem. Sens. 10(4) 045013 doi: 10.1117/1.JRS.10.045013
Published in: Journal of Applied Remote Sensing Volume 10, Issue 4
Show Author Affiliations
Bo Yu, Institute of Remote Sensing and Digital Earth (China)
Fang Chen, Institute of Remote Sensing and Digital Earth (China)


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