A generic framework for the development of geospatial processing pipelines on clusters
Type de document
Article de revue scientifique à comité de lecture
Affiliation de l'auteur
IRSTEA MONTPELLIER UMR TETIS FRA
Résumé / Abstract
The amount of remote sensing (RS) data available for applications is constantly growing due to the rise of very high resolution sensors and short-repeat-cycle satellites. Consequently, tackling the computational complexity in Earth observation information extraction is rising as a major challenge. Resorting to high-performance computing (HPC) is becoming a common practice, since this provides environments and programming facilities that are able to speed up processes. In particular, clusters are flexible cost-effective systems that are able to perform data-intensive tasks ideally fulfilling any computational requirement. However, their use typically implies a significant coding effort to build proper implementations of specific processing pipelines. This letter presents a generic framework for the development of RS images processing applications targeting cluster computing. It is based on common open-source libraries and leverages the parallelization of a wide variety of image processing pipelines in a transparent way. Performances on typical RS tasks implemented using the proposed framework demonstrate a great potential for the effective and timely processing of large amount of data.
IEEE Geoscience and Remote Sensing Letters, vol. 13, num. 11, p. 1706 - 1710