Large-scale landslide detection for practical use based on image saliency

Bo Yu; Fang Chen

doi:10.1117/1.JRS.10.045013

5 November 2016 Large-scale landslide detection for practical use based on image saliency

Bo Yu, Fang Chen

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 4, 045013 (November 2016). https://doi.org/10.1117/1.JRS.10.045013

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.

Citation Download Citation

Bo Yu and Fang Chen "Large-scale landslide detection for practical use based on image saliency," Journal of Applied Remote Sensing 10(4), 045013 (5 November 2016). https://doi.org/10.1117/1.JRS.10.045013

Published: 5 November 2016

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