FN Archimer Export Format
PT J
TI TOWARDS TIME-SERIES PROCESSING OF VHR SATELLITE IMAGES FOR SURFACE DEFORMATION DETECTON AND MEASUREMENTS
BT 
AF STUMPF, A.
   DELACOURT, Christophe
   MALET, J. P.
AS 1:1,2,3;2:2;3:1;
FF 1:;2:;3:;
C1 Univ Strasbourg EOST, CNRS UMR 7516, Inst Phys Globe Strasbourg, 5 Rue Descartes, F-67084 Strasbourg, France.
   Univ Western Brittany, Inst Univ Europeen Mer, CNRS UMR 6538, Lab Domaines Ocean, F-29280 Plouzane, France.
   Univ Strasbourg, CNRS UMR 7362, Lab Image, Ville,Environm, F-67000 Strasbourg, France.
C2 UNIV STRASBOURG, FRANCE
   UBO, FRANCE
   UNIV STRASBOURG, FRANCE
UM LGO
IN DOAJ
TC 1
UR https://archimer.ifremer.fr/doc/00639/75146/75512.pdf
LA English
DT Article
DE ;deformation measurement;image correlation;high-performance computing;satellite remote sensing
AB 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. 
PY 2015
SO Isprs Geospatial Week 2015
SN 1682-1750
PU Copernicus Gesellschaft Mbh
VL 40-3
IS W3
UT 000382327100084
BP 595
EP 599
DI 10.5194/isprsarchives-XL-3-W3-595-2015
ID 75146
ER

EF