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A Novel Approach to EO, IR, SAR, and Hyperspectral Sensor Fusion

Award Information

Department of Defense
Air Force
Award ID:
Program Year/Program:
2005 / SBIR
Agency Tracking Number:
Solicitation Year:
Solicitation Topic Code:
Solicitation Number:
Small Business Information
Intelligent Automation, Inc.
15400 Calhoun Drive Suite 400 Rockville, MD 20855-
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Woman-Owned: Yes
Minority-Owned: No
HUBZone-Owned: No
Phase 1
Fiscal Year: 2005
Title: A Novel Approach to EO, IR, SAR, and Hyperspectral Sensor Fusion
Agency / Branch: DOD / USAF
Contract: FA8718-05-C-0057
Award Amount: $100,000.00


Target detection and classification is challenging because target detection and classification cannot be effectively carried out by pure visual inspection and must rely on some automatic algorithms. Intelligent Automation, Inc. (IAI) and its subcontractor, Prof. C. Chang of the University of Maryland at Baltimore County (UMBC), propose a new hybrid framework for target detection and classification using airborne images from two perspectives: spatial and spectral. The spatial approach focuses on texture analysis. The idea is motivated by the fact that the image texture will be disturbed if targets are present. This approach is applicable to EO, IR, hyperspectral, and SAR images. The spectral approach and has two steps. First, a new automatic target generation process (ATGP) is used to generate a set of potential targets from the image data in an unsupervised fashion without using any prior knowledge. Second, an Automatic Target Detection and Classification Algorithm (ATDCA) is used to identify the potential targets. The algorithm can be used to detect anomalies (new and unknown targets) in blind environments. The algorithm is suitable for surveillance operations where the objective is to detect the presence of any potential targets.

Principal Investigator:

Chiman Kwan
Director of Research and Design

Business Contact:

Mark James
Contracts and Proposals Manager
Small Business Information at Submission:

15400 Calhoun Drive, Suite 400 Rockville, MD 20855

EIN/Tax ID: 521497192
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No