Fractal Analysis of Multi-Band IR Imagery for Target Acquisition

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$49,456.00
Award Year:
1993
Program:
SBIR
Phase:
Phase I
Contract:
n/a
Agency Tracking Number:
20629
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
Atlantic Aerospace
470 Trotten Pond Road, Waltham, MA, 02154
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
Tamar Peli
(617) 890-4200
Business Contact:
() -
Research Institution:
n/a
Abstract
It is proposed to develop and demonstrate a new signal processing approach for acquiring low contrast military targets in multi-band IR imagery. This new computationally efficient processing methodology has been shown to provide significant better detection/discrimination results in single-band long wave FLIR imagery as well as in the acoustic, SAR and visible optics domains. Atlantic Aerospace has emerged as the technical leader in developing and applying this technology to defense-related problem areas. We believe that the same generic approach appropriately modified to reflect the structural and textural characteristics of high value targets and typical backgrounds in multi-band IR imagery, will provide significant new capability and substantial performance gains for automatic target acquisition. In this Phase I program wwe will utilize our previously develooped morphology-based detection algorithm, that applies both size and amplitude constraints at the pixel level, to identify potential areas of interest in multi-band IR imagery. For each of the detected areas we will perform fractal analysis independently in the available spectral bands. Our analysis methods will include the evaluation of fractal and high order signatures. The potential capability of the developed measures to automatically cue an operator or a fully automated process to areas of high value targets will be demonstrated. Although the primary interest of this Phase I program is on developing fractal-based measures for the discrimination of man-made objects from natural backgrounds, we will also analyze large patches of natural background and characterize their fractal properties in each spectral band. The emphasis will be on parameterization most appropriate for scene generation.

* information listed above is at the time of submission.

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