Devin White, Senior Researcher at Oak Ridge National Laboratory shares how they are using GPUs to improve the geolocation accuracy of imagery collected by a satellite, manned aircraft, or an unmanned aerial system.
Using Tesla K80 GPUs and CUDA, the researchers in the Geographic Information Science and Technology Group at ORNL developed a sensor-agnostic, plugin-based framework to support photogrammetric and computer vision processing tasks like image registration and orthorectification.
Read about Devin’s work in more detail on his recent GPU Computing Spotlight interview on Parallel Forall.
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Share Your Science: Improving the Geolocation Accuracy of Aerial and Orbital Imagery with GPUs
Sep 07, 2016
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AI-Generated Summary
- Researchers at Oak Ridge National Laboratory are using GPUs to improve the geolocation accuracy of imagery collected by satellites, manned aircraft, or unmanned aerial systems.
- Devin White, a Senior Researcher, developed a sensor-agnostic framework using Tesla K80 GPUs and CUDA to support tasks like image registration and orthorectification.
- The framework is plugin-based and supports photogrammetric and computer vision processing tasks.
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