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Topological Data Analysis for Automated Annotation of EO/SAR Datasets

Award Information
Agency: Department of Defense
Branch: National Geospatial-Intelligence Agency
Contract: HM047621C0023
Agency Tracking Number: NGA-P1-21-08
Amount: $100,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: NGA203-005
Solicitation Number: 20.3
Solicitation Year: 2020
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-06-14
Award End Date (Contract End Date): 2022-03-27
Small Business Information
9301 Corbin Avenue Suite 2000
Northridge, CA 91324-1111
United States
DUNS: 082191198
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Erford Porter
 (703) 885-8780
Business Contact
 Greg Fetzer
Phone: (303) 651-6756
Research Institution

In recent years, it has become increasingly important to conduct Geospatial Intelligence (GEOINT) operation via commercial and government persistent sensor systems, which have produced a copious amount of data relevant to the National Geospatial-Intelligence Agency (NGA). As the supply of data expands, it is necessary to employ automated analytics to exploit the data efficiently. We cannot rely on sheer human power to analyze data; instead, results must be automated and human analysts cued for final judgment. While there has been successful empirical application of AI algorithms on complex image domain problems (e.g., target/facial recognition), the mathematical basis has not kept pace, nor has there been a similar success in more difficult sensor modalities such as Synthetic Aperture Radar (SAR). Once trained, the current state of the art in AI is fragile on new data and methods are often ad hoc, not relying on technical/empirical rigor. For NGA, these issues are a significant impediment to ensuring reliability, explainability, and trustworthiness of neural networks, thus failing to achieve the trusted autonomy required to deliver on NGA's AAA Strategy. In high stakes environments of DoD applications, this situation is intolerable as the United States races for AI dominance. In response to this solicitation, Arete has developed a unique mathematical approach to dramatically reduce the human time required in AI systems based on recent advances in topological data analysis. Our method is also robust to noisy data with widely different views. During Phase I, we will perform a feasibility study, which will lead to the Homological Object Recognizer and Nomenclature Extraction Toolkit (HORNET) operating on SAR in Phase II.

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

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