Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/43998
Title: Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method
Authors: Li, Z 
Shi, W 
Myint, SW
Lu, P
Wang, Q
Keywords: Aerial orthophoto
Change detection
Change vector analysis (CVA)
Landslide inventory mapping (LIM)
Level set evolution (LSE)
Issue Date: 2016
Publisher: Elsevier
Source: Remote sensing of environment, 2016, v. 175, p. 215-230 How to cite?
Journal: Remote sensing of environment 
Abstract: Landslide inventory mapping (LIM) is an increasingly important research topic in remote sensing and natural hazards. Past studies achieve LIM mainly using on-screen interpretation of aerial photos, and little attention has been paid to developing more automated methods. In recent years, the use of multitemporal remote sensing images makes it possible to map landslides semi-automatically. Although numerous methods have been proposed, only a few methods are competent for some specific situations and there is large room for improvement in their degree of automation. For these reasons, a semi-automated approach is proposed for reliable and accurate LIM from bitemporal aerial orthophotos. Specifically, it consists of two principal steps: 1) change detection-based thresholding (CDT) and 2) level set evolution (LSE). CDT is mainly used to generate the initial zero-level curve (ZLC) for LSE, thus automating the proposed method considerably. It includes three substeps: 1) generating difference image (DI) using change vector analysis (CVA), 2) detecting landslide candidates using a thresholding method, and 3) removing errors using morphology operations. Then, landslide boundaries are detected using two types of LSE, i.e., edge-based LSE (ELSE) and region-based LSE (RLSE). Finally, the effectiveness and advantages of the proposed methods are corroborated by a series of experiments. Given its efficiency and accuracy, it can be applied to rapid responses of natural hazards. This study is the first attempt to apply LSE to LIM from bitemporal remote sensing images.
URI: http://hdl.handle.net/10397/43998
ISSN: 0034-4257
EISSN: 1879-0704
DOI: 10.1016/j.rse.2016.01.003
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