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Target Identification Using Machine Learning-Based Fusion of Hyperspectral and 3D Imagery

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8649-20-P-0440
Agency Tracking Number: F192-026-0421
Amount: $149,999.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: AF192-026
Solicitation Number: 19.2
Timeline
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-03-06
Award End Date (Contract End Date): 2020-12-06
Small Business Information
4 Fourth Avenue
Burlington, MA 01803
United States
DUNS: 047627732
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Sandra Wiseman
 Senior Scientist
 (781) 273-4770
 swiseman@spectral.com
Business Contact
 Marsha J. Fox
Phone: (781) 273-4770
Email: mfox@spectral.com
Research Institution
N/A
Abstract

Target detection and identification in the Hyperspectral Imaging (HSI) domain relies on the uniqueness of spectral signatures, whereas classification in the spatial domain relies on morphological attributes of the target. Therefore, a detection and classification approach that fuses information from the spectral and spatial domains has the potential to considerably increase target detection confidence and reduce false positives. In Phase I, we will prototype a new spectral-spatial fusion processing chain that represents a significant advance over existing methods, which were developed mainly for surface and terrain classification. Target Bidirectional Reflectance Distribution Function (BDRF) and shadow effects are accounted for using advanced HSI target detection algorithms to produce provisional detection scores. Potential target-containing regions of interest (ROIs) are further identified using 3-D shape information contained in Digital Surface Models. Target detections and identifications are made with a machine learning algorithm followed by fusion with HSI scores. The Phase I work, conducted in collaboration with the RIT Center for Imaging Science, will provide a proof-of-principle for the development of deliverable software in Phase II.

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

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