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2017 GTC Washington DC

DC7119 - High Temporal Resolution ISR Data and Synergistic Decisions through Multi-Stream Deep Learning

Session Speakers
  • William Rorrer - Program Manager, Harris

    Will Rorrer has worked with the Harris Corporation for over 15 years providing management and guidance to key business units. These areas include the Night Vision operations team, the Jagwire program for streaming, cataloging, and analyzing full-motion video, and leading research and development on deep learning tools and applications. Throughout his career, Will has been honored to support the National Geospatial Intelligence Agency and other parts of the Department of Defense in using high-tech capabilities for solving global security problems.

Session Description

We'll cover research Harris has performed in two applications of deep learning leveraging NVIDIA GPUs. The first topic pertains to high temporal resolution datasets. Many of the "smallsat" providers are offering imagery collections at extremely high temporal resolution. Deep learning provides the ability to automate much of the routine analysis of this data. The second topic will walk through several use cases Harris has developed harnessing multiple input sources for deep learning. Deep learning has been applied to automatic feature extraction for a variety of individual data types such as imagery, point clouds, and video. One of the less explored benefits of deep learning in remote sensing data is the ability to incorporate multiple streams of data into the same neural network, revealing more information than either of the individual data types can provide alone.

Additional Information
AI for Accelerated Analytics, Deep Learning and AI
50 minutes
Session Schedule