Paper Info
Title
Landslide Mapping From PlanetScope Images Using Improved Region-Based Level Set Evolution
Abstract
A timely and reliable inventory is essential for landslide hazard assessment and risk management. In this study, we use images from PlanetScope, which provides global 3 m daily Earth observations, for rapid mapping of landslide inventory. We propose a semiautomated method that combines change detection and region-based level set evolution (RLSE) to improve landslide mapping efficiency. Our approach uses change detection methods of independent component analysis (ICA), principal component analysis (PCA), and change vector analysis (CVA) for automated generation of landslide zero-level curves (ZLCs), and then incorporates the RLSE method to refine landslide mapping results. To corroborate the applicability of the proposed method, we test the landslide mapping performance on the Kodagu event (India, 2018) using ICA-, PCA- and CVA-based RLSE. The results show that ICA-based RLSE can achieve better landslide mapping accuracy in terms of completeness, correctness, and the Kappa coefficient. This study demonstrates the suitability and potential of low-orbit miniature satellites such as PlanetScope for landslide mapping. To the best of our knowledge, it is the first attempt to incorporate PlanetScope images and the change detection-based RLSE method for landslide mapping.
Year
DOI
Venue
2022
10.1109/LGRS.2021.3122964
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Keywords
DocType
Volume
Terrain factors, Principal component analysis, Level set, Rocks, Satellite broadcasting, Spatial resolution, Satellites, Change vector analysis (CVA), independent component analysis (ICA), landslide mapping, PlanetScope, principal component analysis (PCA), region-based level set evolution (RLSE)
Journal
19
ISSN
Citations 
PageRank 
1545-598X
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Ping Lu101.01
Wenyang Shi200.34
Zhongbin Li300.34