Type Article
Date 2015
Language English
Author(s) Stumpf A.1, 2, 3, Delacourt Christophe2, Malet J. P.1
Affiliation(s) 1 : Univ Strasbourg EOST, CNRS UMR 7516, Inst Phys Globe Strasbourg, 5 Rue Descartes, F-67084 Strasbourg, France.
2 : Univ Western Brittany, Inst Univ Europeen Mer, CNRS UMR 6538, Lab Domaines Ocean, F-29280 Plouzane, France.
3 : Univ Strasbourg, CNRS UMR 7362, Lab Image, Ville,Environm, F-67000 Strasbourg, France.
Meeting ISPRS Geospatial Week, La Grande Motte, FRANCE, SEP 28-OCT 03, 2015
Source Isprs Geospatial Week 2015 (1682-1750) (Copernicus Gesellschaft Mbh), 2015 , Vol. 40-3 , N. W3 , P. 595-599
DOI 10.5194/isprsarchives-XL-3-W3-595-2015
WOS© Times Cited 1
Keyword(s) deformation measurement, image correlation, high-performance computing, satellite remote sensing

The increasing fleet of VHR optical satellites (e.g. Pleiades, Spot 6/7, WorldView-3) offers new opportunities for the monitoring of surface deformation resulting from gravitational (e.g. glaciers, landslides) or tectonic forces (coseismic slip). Image correlation techniques have been developed and successfully employed in many geoscientific studies to quantify horizontal surface deformation at sub-pixel precision. The analysis of time-series, however, has received less attention in this context and there is still a lack of techniques that fully exploit archived image time-series and the increasing flux of incoming data. This study targets the development of an image correlation processing chain that relies on multiple pair-wise matching to exploit the redundancy of deformation measurements recorded at different view angles and over multiple time steps. The proposed processing chain is based on a hierarchical image correlation scheme that readily uses parallel processing. Since pair-wise matching can be performed independently the distribution of individual tasks is straightforward and yields to significant reductions of the overall runtime scaling with the available HPC infrastructure. We find that it is more convenient to implement experimental analytical tasks in a high-level programming language (i.e. R) and explore the use of parallel programming to compensate for performance bottlenecks of the interpreted language. Preliminary comparisons against maps from domain expert suggest that the proposed methodology is suitable to eliminate false detections and, thereby, enhances the reliability of correlation-based detections of surface deformation.

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