Abstract
Abstract. The increasing fleet of VHR optical satellites (e.g. Pléiades, 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.
Highlights
The increasing availability of satellite-based earth observation data offers new opportunities to monitor environmental changes with high spatial and temporal resolution and increases the challenges when developing scientific analysis chains for large volumes of data
The development of scientific prototypes exploiting parallel and distributed computing paradigms remains challenging for domain experts typically working with high-level programming languages such as Python, R and Matlab. In this contribution we report on our experience and current efforts in the development of a processing chain for the monitoring of surface deformation from time-series of VHR satellite images (i.e. Pléiades)
Sub-pixel image correlation of optical satellite images has been demonstrated to be a valuable tool for measuring surface deformation such as co-seismic slip (Leprince et al 2007), glacier flow (Heid and Kääb 2012) or landslides (Delacourt et al 2004; Stumpf et al 2014b)
Summary
The increasing availability of satellite-based earth observation data offers new opportunities to monitor environmental changes with high spatial and temporal resolution and increases the challenges when developing scientific analysis chains for large volumes of data. Space agencies and private data providers are already solving resulting challenges regarding data storage, standard level processing and distribution (Dech et al.) and the scientific community is increasingly exploiting tools for image analysis in highperformance computing (HPC) environments (Lee et al 2011; Plaza et al 2011a; Plaza et al 2011b).
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