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Multi-Path Fusion: A Hierarchical Machine Learning Approach for Combining Diverse Data Sets for A Forest Monitoring New Observing System
Document ID
20230009577
Acquisition Source
Goddard Space Flight Center
Document Type
Conference Paper
Authors
James MacKinnon (Goddard Space Flight Center Greenbelt, Maryland, United States)
David Harding (Goddard Space Flight Center Greenbelt, Maryland, United States)
Mark Moussa (Goddard Space Flight Center Greenbelt, Maryland, United States)
Matt Brandt (Goddard Space Flight Center Greenbelt, Maryland, United States)
Paul Montesano (Adnet Systems (United States) Bethesda, Maryland, United States)
Mark Carroll (Goddard Space Flight Center Greenbelt, Maryland, United States)
Randolph Wynne (Virginia Tech Blacksburg, Virginia, United States)
Valerie Thomas (Virginia Tech Blacksburg, Virginia, United States)
Fred Huemmrich (University of Maryland, Baltimore County Baltimore, Maryland, United States)
Jon Ranson (Goddard Space Flight Center Greenbelt, Maryland, United States)
Date Acquired
June 27, 2023
Publication Date
July 16, 2023
Publication Information
Publication: The International Geoscience and Remote Sensing Symposium
Subject Category
Earth Resources and Remote Sensing
Meeting Information
Meeting: The International Geoscience and Remote Sensing Symposium
Location: Pasadena, CA
Country: US
Start Date: July 16, 2023
End Date: July 21, 2023
Sponsors: Institute of Electrical and Electronics Engineers
Funding Number(s)
WBS: 430728.02.21.51.09
OTHER: NNH21ZDA001N-AIST
Distribution Limits
Public
Copyright
Portions of document may include copyright protected material.
Technical Review
Single Expert
Keywords
Data fusion, Data science, deep learning

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