Extracting sinkhole features from time-series of TerraSAR-X/TanDEM-X data

Sinkholes are significant geologic hazards that are mainly formed in water-soluble carbonate bedrocks such as limestone, dolomite or gypsum. Sinkhole formation causes the surface to subside or collapse suddenly without any prior warning, and therefore can lead to extensive damage and even loss of li...

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Bibliographic Details
Published in:ISPRS journal of photogrammetry and remote sensing 2019-04, Vol.150, p.274-284
Main Authors: Vajedian, Sanaz, Motagh, Mahdi
Format: Article
Language:English
Subjects:
Bistatic interferometry
CCA analysis
Sinkhole
TanDEM-X
Wavelet decomposition
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Summary:Sinkholes are significant geologic hazards that are mainly formed in water-soluble carbonate bedrocks such as limestone, dolomite or gypsum. Sinkhole formation causes the surface to subside or collapse suddenly without any prior warning, and therefore can lead to extensive damage and even loss of life and property. Delineating sinkholes is important for understanding hydrological processes and mitigating geological hazards in karst areas. The recent development in deriving high-resolution digital elevation models from space missions such as TerraSAR-X/TanDEM-X (TSX/TDX) enables us to delineate and analyze geomorphologic features and landscape structures at small scale (up to 2 m). In this study we use time-series of TSX/TDX data and develop an adaptive sinkhole-analysis method using interferometry observations. A wavelet-based refinement approach is implemented on interferomeric processing to reduce the baseline bias effects and align the interferometrically-derived DEMs. The multi-temporal DEMs are then successfully stacked using Canonical Correlation Analysis (CCA) to reconstruct a higher quality DEM. Finally, feature extraction using watershed algorithm is applied to precisely delineate geomorphometric characteristics of the sinkholes. Five TSX/TDX images are selected to evaluate the performance of our approach for sinkholes in Hamedan, West Iran. Results show that applying our methodology on high-resolution TSX/TDX data from different geometries and time periods enables us to effectively distinguish sinkholes from other depression features of the basin. Different TSX/TDX pairs produce consistent results for diameter and depth of sinkholes with the standard deviation of approximately 1 m, in agreement with field observations.
ISSN:0924-2716
1872-8235
DOI:10.1016/j.isprsjprs.2019.02.016