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A Multi-Branch Network for Automated VNIIRS Assessment of Motion Imagery

Award Information
Agency: Department of Defense
Branch: National Geospatial-Intelligence Agency
Contract: HM047619C0094
Agency Tracking Number: NGA-P1-19-11
Amount: $100,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: NGA191-003
Solicitation Number: 19.1
Solicitation Year: 2019
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-08-28
Award End Date (Contract End Date): 2020-06-02
Small Business Information
6800 Cortona Drive
Goleta, CA 93117
United States
DUNS: 054672662
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Timothy E Fair
 Senior Analyst
 (805) 968-6787
Business Contact
 SBIR Coordinator
Phone: (805) 968-6787
Research Institution

Due to the lack of consistency in existing automated methods for assigning VNIIRS levels to motion imagery, and the overwhelming human resources required to manually assign levels, a new method of automated/semi-automated VNIIRS assessment is needed. In recent years, advancements in deep learning have provided solutions to previously intractable computer vision problems. In many cases, automated deep learning algorithms have been able to meet and in some cases surpass human analysis performance for well-defined object classification tasks. Deep learning algorithms are also extremely efficient and offer scalability for solving challenging large scale problems. For these reasons, a deep learning solution should be considered. Toyon proposes a automating the assignment of VNIIRS labels to motion imagery by using a combining motion detection, object and event classification that will provide information to a final classification a network that decides the VNIIRS level of a motion imagery clip. These classification techniques leverage a novel multi-branch convolutional neural network and state-of-the-art long-short term memory networks. The system will enable intermediate textual outputs to be displayed to analysts to improve their understanding of the rating and give additional order of battle context for the processed motion imagery.

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