SBIR-STTR Award

Low-shot Using Contextual Knowledge and Ephemeral Search Hierarchy for One-shot Targets (LUCK E SHOT) in Remote Sensing Imagery
Award last edited on: 8/1/2019

Sponsored Program
SBIR
Awarding Agency
DOD : NGA
Total Award Amount
$1,100,000
Award Phase
2
Solicitation Topic Code
NGA181-010
Principal Investigator
Timothy E Fair

Company Information

Toyon Research Corporation (AKA: Data Tools for Citizen Science)

6800 Cortona Drive
Goleta, CA 93117
   (805) 968-6787
   toyoninfo@toyon.com
   www.toyon.com
Location: Multiple
Congr. District: 24
County: Santa Barbara

Phase I

Contract Number: HM047618C0068
Start Date: 9/11/2018    Completed: 6/15/2019
Phase I year
2018
Phase I Amount
$100,000
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.

Phase II

Contract Number: HM047620C0018
Start Date: 6/15/2020    Completed: 6/30/2020
Phase II year
2020
Phase II Amount
$1,000,000
At a high level, the primary technical objective of the proposed effort is to Identify, obtain, evaluate, and improve datasets; Investigate the use of Meta-Learning to improve the low-shot detection performance of the feed forward network; Extend research in multi-branch multi-domain embedding networks and semantic layouts for low-shot detection in remote sensing imagery; and Develop and implement a feedback mechanism to process nearby geographic regions in areas where low-shot objects are confirmed. Use the tool (blender rendering tool for 3D CAD models) to build CAD models to be rendered from any perspective, resolution, and with a background model and point spread function representative of remote sensing imagery.