Exploiting Different Types of Parallelism in Distributed Analysis of Remote Sensing Data

The vast amount of data obtained from current remote sensing data acquisition technologies represents a wealth of useful and affordable geospatial data for policy and decision makers. However, the consequent computational cost of analyzing these data may become prohibitive. This letter extends previous efforts in exploiting distributed processing to speed up the image interpretation process. In this letter, we propose and evaluate a mechanism to exploit task parallelism in addition to data parallelism. Experiments conducted on cloud computing infrastructure, following an object-based interpretation model, demonstrated that substantial performance gains can be obtained with the proposed mechanism.