Generalized Change Detection to Cue Regions of Interest

Award Information
Agency: Department of Defense
Branch: National Geospatial-Intelligence Agency
Contract: HM047618C0041
Agency Tracking Number: NGA-P1-18-11
Amount: $99,900.00
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): 2018-09-21
Award End Date (Contract End Date): 2019-06-15
Small Business Information
101 Main Street, Cambridge, MA, 02142
DUNS: 081021992
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Jarred Barber
 (717) 991-4442
 jarred.barber@featurex.ai
Business Contact
 Michael Hayes
Phone: (646) 812-7548
Email: michael.hayes@featurex.ai
Research Institution
N/A
Abstract
Generalized change detection is a critical capability to mitigate the need for massive human inspection of the rapidly expanding volume ofglobal overhead satellite imagery. Current optical change detection approaches focus on fully specified systems to detect a predefined set ofchanges, and effective approaches for generalized change detection have not yet been demonstrated. We propose to build a deep learningbasedchange detection system that operates at the level of "intermediate semantics" for satellite images. Intermediate semantics are animage representation above the level of pixels, which vary considerably from day to day, but below the semantics of objects. Our system willlearn intermediate image semantics by discovering latent representations that can recreate the consistent aspects of images, even in thepresence of superficial changes such as changes in lighting. Our system will also have a trained discriminator to determine which changes in theintermediate semantics suggest meaningful change.Our approach is self-supervised: only time and place are used to provide training signals, and we will not use human-labeled training data.This approach will allow us to find interesting and significant changes over time, cueing regions of interest for analysts without needing to trainfor specific changes to detect.

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

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