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Generalized Change Detection to Cue Regions of Interest

Award Information
Agency: Department of Defense
Branch: National Geospatial-Intelligence Agency
Contract: HM047618C0047
Agency Tracking Number: NGA-P1-18-18
Amount: $100,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: NGA181-006
Solicitation Number: 2018.1
Timeline
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-09-10
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
 Matthew Buoni
 (805) 968-6787
 mbuoni@toyon.com
Business Contact
 Marcella Lindbery
Phone: (805) 968-6787
Email: mlindbery@toyon.com
Research Institution
N/A
Abstract
Toyon Research Corporation proposes to research and develop algorithms for generalized change detection, by leveraging and exploringexisting and proven effective traditional and deep learning methods, with a unique 3D reconstruction component. The vast majority of themassive amounts of imagery data will have small pixel level differences due to a multitude of unimportant changes: minor misregistration,changes in lighting or occlusions, variations in weather and vegetation, etc. Toyons proposed effort addresses these challenges throughapplication of a Gaussian Mixture Model (GMM) learned via expectation maximization (EM). Within the GMM framework, we will make use ofboth standard and deep learning based features, as well as 3D model height information when available. Once salient change has beendetected, we will classify it based on examples provided by the NGA and obtained during data curation for the area of interest (AOI) wheredetection occurred. Classification will be performed by features learned via Transfer Learning from one of the large and successful CNNs(VGG16, Resnet50, Inception) and those extracted from autoencoders. Classes with few examples will make use of data augmentation, and wewill explore Generative Adversarial Networks (GANs), which have promise to be an effective and innovative exemplar generator scheme.

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

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