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Low-Shot Detection in Remote Sensing Imagery

Award Information
Agency: Department of Defense
Branch: National Geospatial-Intelligence Agency
Contract: HM047618C0068
Agency Tracking Number: NGA-P1-18-10
Amount: $100,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: NGA181-010
Solicitation Number: 2018.1
Timeline
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-09-11
Award End Date (Contract End Date): 2019-06-15
Small Business Information
6800 Cortona Drive, Goleta, CA, 93117
DUNS: 054672662
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Timothy Fair
 (805) 968-6787
 tfair@toyon.com
Business Contact
 Marcella Lindbery
Phone: (805) 968-6787
Email: mlindbery@toyon.com
Research Institution
N/A
Abstract
The National Geospatial-Intelligence Agency (NGA) ingests and analyzes raw imagery from multiple sources to form actionable intelligenceproducts that can be disseminated across the intelligence community (IC). To effectively meet these demands NGA must continue to improveits automated and semi-automated methods for target detection and classification. Of particular concern is furthering NGA's ability to identifyand locate rare intelligence targets of interest within large search regions in remote sensing imagery. This is known as the low-shot problem.Deep learning methods continue to demonstrate state-of-the-art performance in the problem domains of computer vision and machinelearning, including for low-shot detection. However, only recently have these deep networks been applied to the remote sensing domain.Challenges exist for adapting such deep learning approaches to the remote sensing domain including lack of large corpuses of training dataand significant differences in image appearance when compared to traditional deep learning problems and datasets. Toyon proposes theresearch and development a low-shot detection algorithm that relies on deep learning to; produce a feature representation specific to theremote sensing domain, leverage 3D class and image information jointly, generate realistic hallucinated imagery of low-shot classes.

* Information listed above is at the time of submission. *

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