High-performance time-series quantitative retrieval from satellite images on a GPU cluster
- Submitting institution
-
University of Derby
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 783961-1
- Type
- D - Journal article
- DOI
-
10.1109/JSTARS.2019.2920077
- Title of journal
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Article number
- -
- First page
- 2810
- Volume
- 12
- Issue
- 8
- ISSN
- 1939-1404
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2019
- URL
-
https://ieeexplore.ieee.org/document/8760407
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
-
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work demonstrates the effectiveness of exploiting inherent parallelism in temporal remote sensing data using a multi-layered analysis model for GPU-based parallel processing. This is significant as time, power, and cost-efficient processing of large remote sensing data sets is vital to large scale temporal projects such as land use analysis. Through separating data into temporal, spatial, and feature complexity layers; analysis can be performed using commercial rather than bespoke GPU-based cloud environments. A multi-level parallelism approach allows this technique to be applied to a variety of data sets and to heterogeneous processing environments with little impact on efficiency or schedulability.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
