Scipediacontent: Scipediacontent moved page Draft Content 297762687 to Hagemeier et al 2018a

2021-01-28T16:29:55Z

Scipediacontent moved page Draft Content 297762687 to Hagemeier et al 2018a

Scipediacontent: Created page with " == Abstract == The progress of remote sensing technologies leads to increased supply of high-resolution image data. However, solutions for processing large volumes of data a..."

2021-01-28T16:29:52Z

Created page with " == Abstract == The progress of remote sensing technologies leads to increased supply of high-resolution image data. However, solutions for processing large volumes of data a..."

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== Abstract ==

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.

== Original document ==

The different versions of the original document can be found in:

* [http://juser.fz-juelich.de/record/857588/files/igarss-2018-urc.pdf http://juser.fz-juelich.de/record/857588/files/igarss-2018-urc.pdf]

* [http://xplorestaging.ieee.org/ielx7/8496405/8517275/08519364.pdf?arnumber=8519364 http://xplorestaging.ieee.org/ielx7/8496405/8517275/08519364.pdf?arnumber=8519364],
: [http://dx.doi.org/10.1109/igarss.2018.8519364 http://dx.doi.org/10.1109/igarss.2018.8519364]

* [https://dblp.uni-trier.de/db/conf/igarss/igarss2018.html#MemonCHRN18 https://dblp.uni-trier.de/db/conf/igarss/igarss2018.html#MemonCHRN18],
: [https://ieeexplore.ieee.org/document/8519364 https://ieeexplore.ieee.org/document/8519364],
: [https://doi.org/10.1109/IGARSS.2018.8519364 https://doi.org/10.1109/IGARSS.2018.8519364],
: [http://juser.fz-juelich.de/record/857588 http://juser.fz-juelich.de/record/857588],
: [https://academic.microsoft.com/#/detail/2900895905 https://academic.microsoft.com/#/detail/2900895905]

* [https://juser.fz-juelich.de/record/857588 https://juser.fz-juelich.de/record/857588],
: [https://juser.fz-juelich.de/search?p=id:%22FZJ-2018-06573%22 https://juser.fz-juelich.de/search?p=id:%22FZJ-2018-06573%22]

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Hagemeier et al 2018a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 297762687 to Hagemeier et al 2018a

Scipediacontent: Created page with " == Abstract == The progress of remote sensing technologies leads to increased supply of high-resolution image data. However, solutions for processing large volumes of data a..."