In:
Photogrammetric Engineering & Remote Sensing, American Society for Photogrammetry and Remote Sensing, Vol. 83, No. 5 ( 2017-05-01), p. 351-363
Abstract:
Effective large-scale landslide mapping is becoming significantly important for analyzing natural hazards and providing landslide locations rapidly for emergency response. Change detection and machine learning methods are commonly used for landslide detection. Change detection mostly
relies on several experienced parameters that users have to tune for different images, which limits the practical application. The training machine learning model consumes much time, and it is limited to specific imaging conditions. In this paper, a simple method for landslide detection using a fixed parameter by calculating image saliency is proposed. Landslide is detected as a saliency object within the background of vegetation and bare rocks. It is fast and robust for the experimental images, and outperforms the state-of-the-art, semi-automatic method in terms of accuracy and
computing time. Given the high efficiency and robustness of the proposed method, it is applicable to practical cases for hazard estimation.
Type of Medium:
Online Resource
ISSN:
0099-1112
DOI:
10.14358/PERS.83.5.351
Language:
English
Publisher:
American Society for Photogrammetry and Remote Sensing
Publication Date:
2017
detail.hit.zdb_id:
2317128-5
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