Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10352410 | Computers & Geosciences | 2011 | 5 Pages |
Abstract
As the quality and accuracy of remote-sensing instruments improve, the ability to quickly process remotely sensed data is in increasing demand. Quantitative remote-sensing retrieval is a complex computing process because of the terabytes or petabytes of data processed and the tight-coupling remote-sensing algorithms. In this paper, we intend to demonstrate the use of grid computing for quantitative remote-sensing retrieval applications with a workload estimation and task partition algorithm. Using a grid workflow for the quantitative remote-sensing retrieval service is an intuitive way to use the grid service for users without grid expertise. A case study showed that significant improvement in the system performance could be achieved with this implementation. The results of the case study also give a perspective on the potential of applying grid computing practices to remote-sensing problems.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Yong Xue, Jianwen Ai, Wei Wan, Huadong Guo, Yingjie Li, Ying Wang, Jie Guang, Linlu Mei, Hui Xu,