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Deep Learning Framework for Waveband-Specific Global Scene Background Generation

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8651-23-C-A003
Agency Tracking Number: F212-0002-0201
Amount: $1,250,000.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: AF212-0002
Solicitation Number: 21.2
Timeline
Solicitation Year: 2021
Award Year: 2023
Award Start Date (Proposal Award Date): 2022-12-19
Award End Date (Contract End Date): 2025-03-22
Small Business Information
6820 Moquin Dr NW
Huntsville, AL 35806-2900
United States
DUNS: 185169620
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Jackson Cornelius
 (662) 643-7949
 jackson.cornelius@cfdrc.com
Business Contact
 Tanu Singhal
Phone: (256) 361-0799
Email: contracts@cfdrc.com
Research Institution
N/A
Abstract

The proposed effort aims to develop a deep learning framework for waveband-specific and geospecific background generation to address US Air Force’s need to create a global, multispectral background scene database to support scene generation. Using deep neural networks and collections of Geographic information system (GIS) data, we propose to develop a tool for background database generation that automates the extraction scene background features within a user-defined Area of Interest (AOI) on Earth, leverages this information to resolve imagery at the apparent resolution of sensors rapidly changing altitudes, and streamlines the preparation of generated backgrounds scenes for processing in FLITES. The proposed solution requires the development of tools to interface with GIS databases for extraction of available vector and raster data; semantic segmentation models to classify land cover into material classes with known physical properties; and texture synthesis and super-resolution models to enhance imagery, simulating reductions in ground sample distance. The Phase II effort will focus on capability extension of algorithms developed in Phase I, large-scale GIS data curation, optimization of algorithms for accuracy and efficiency, maturation of the software tool to enable compatibility and integrability with FLITES, extensive verification and validation, and technology insertion into Air Force workflows.

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

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