This title appears in the Scientific Report :
2018
Please use the identifier:
http://dx.doi.org/10.1109/IGARSS.2018.8519364 in citations.
Please use the identifier: http://hdl.handle.net/2128/20139 in citations.
Automated Analysis of Remotely Sensed Images Using the Unicore Workflow Management System
Automated Analysis of Remotely Sensed Images Using the Unicore Workflow Management System
The progress of remote sensing technologies leads to increased supply of high-resolution image data. However, solutions for processing large volumes of data are lagging behind: desktop computers cannot cope anymore with the requirements of macro-scale remote sensing applications; therefore, parallel...
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Personal Name(s): | Memon, Mohammad Shahbaz (Corresponding author) |
---|---|
Cavallaro, Gabriele / Hagemeier, Bjorn / Riedel, Morris / Neukirchen, Helmut | |
Contributing Institute: |
Jülich Supercomputing Center; JSC |
Imprint: |
IEEE
2018
|
Physical Description: |
1128 - 1131 |
ISBN: |
978-1-5386-7150-4 |
DOI: |
10.1109/IGARSS.2018.8519364 |
Conference: | 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia (Spain), 2018-07-22 - 2018-07-27 |
Document Type: |
Contribution to a book Contribution to a conference proceedings |
Research Program: |
Doktorand ohne besondere Förderung Data-Intensive Science and Federated Computing |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/20139 in citations.
The progress of remote sensing technologies leads to increased supply of high-resolution image data. However, solutions for processing large volumes of data are lagging behind: desktop computers cannot cope anymore with the requirements of macro-scale remote sensing applications; therefore, parallel methods running in High-Performance Computing (HPC) environments are essential. Managing an HPC processing pipeline is non-trivial for a scientist, especially when the computing environment is heterogeneous and the set of tasks has complex dependencies. This paper proposes an end-to-end scientific workflow approach based on the UNICORE workflow management system for automating the full chain of Support Vector Machine (SVM)-based classification of remotely sensed images. The high-level nature of UNICORE workflows allows to deal with heterogeneity of HPC computing environments and offers powerful workflow operations such as needed for parameter sweeps. As a result, the remote sensing workflow of SVM-based classification becomes re-usable across different computing environments, thus increasing usability and reducing efforts for a scientist. |