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Algorithm Performance Evaluation with Low Sample Size

Award Information
Agency: Department of Defense
Branch: National Geospatial-Intelligence Agency
Contract: HM047621C0037
Agency Tracking Number: NGA-P1-21-10
Amount: $99,980.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: NGA20C-001
Solicitation Number: 20.C
Solicitation Year: 2020
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-07-28
Award End Date (Contract End Date): 2022-05-01
Small Business Information
P.O. Box 346
Calumet, MI 49913-1111
United States
DUNS: 803724301
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Timothy Havens
 (906) 487-3115
Business Contact
 Marshall Weathersby
Phone: (906) 337-3360
Research Institution
 Michigan Technological University
 Marilyn Haapapuro
1400 Townsend Drive
Houghton, MI 49931
United States

 (906) 487-2228
 Nonprofit College or University

The team of Signature Research, Inc. and Michigan Technological University will develop and demonstrate methods and metrics to evaluate the performance of machine learning-based computer vision algorithms with low numbers of samples of labeled EO imagery. We will use the existing xView panchromatic dataset to demonstrate a proof-of-concept set of tools. If successful, in Phase II, we will extend the tools and techniques to other signature spectra. The demonstrated capability will include variation across at least two operating conditions, e.g., geographic diversity and object size. We will use two methodologies to try to solve this difficult and complex problem. The two methods will include Network Stability Theory and Mutual Information Theory. In addition to the small labelled dataset, we will augment the testing of the tools with variability in the signatures using SGR-generated synthetic imagery. The Phase I program will result in proof-of-concept performance assessment on the selected data set.

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

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