Densely Connected Neural Networks for Remote Sensing

Award Information
Agency: Department of Defense
Branch: National Geospatial-Intelligence Agency
Contract: HM047618C0062
Agency Tracking Number: NGA-P1-18-14
Amount: $99,999.00
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): 2018-09-17
Award End Date (Contract End Date): 2019-06-15
Small Business Information
2501 Earl Rudder Freeway South, College Station, TX, 77845
DUNS: 184758308
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Christian Bruccoleri
 (979) 764-2200
 christian.bruccoleri@lynntech.com
Business Contact
 Jaclyn McCord
Phone: (979) 764-2200
Email: contract@lynntech.com
Research Institution
N/A
Abstract
The objective of this project is to design a software architecture based on densely-connected neural network to perform automatic targetsegmentation and recognition using training datasets of limited size (low-shot). Deep learning architectures have proved to be extremelyeffective at object detection and recognition, but such capability comes at the cost of having large labeled datasets. Such datasets are notusually available for new threats, which appear continuously in a changing geo-political landscape. The NGA needs to be able to performautomatic target detection and recognition from aerial images when few prior examples of the target are available. The initial Phase I study willfocus on panchromatic Electro-Optical sensors, but the proposed Neural Network architecture is applicable to other types of sensors as well.Transition to other types of sensors is expected in the later stages of the project. The outcome of this project will provide the NGA and the DODwith the much needed capability of being able to detect and react quickly to new threats detectable from a variety of Intelligence, Surveillanceand Reconnaissance payloads.

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

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