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Proceedings Paper

Adaptive multispectral CFAR detection of land mines
Author(s): Quentin A. Holmes; Craig R. Schwartz; John H. Seldin; James A. Wright; Lester J. Witter
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Paper Abstract

An automatic target detection algorithm which exploits spectral and spatial signatures of mines is described. Key features of this approach include the ability to adapt to unknown or changing background statistics and the capability to operate with unknown spectral signatures. Preliminary results of applying this algorithm for surface mine detection in video-based multispectral imagery covering the 400-900 nm region are presented. Tests on actual airborne data collected during 1992, 1993, and 1994 show that at 8-inch ground resolution (with 4x over-sampling), 12-inch diameter circular mines can be discriminated from natural backgrounds with a probability of detection around 85% with 3-4 false alarms per image in a relatively harsh clutter environment. This capability has been shown to be sufficient to meet COBRA minefield requirements during preliminary system testing.

Paper Details

Date Published: 20 June 1995
PDF: 12 pages
Proc. SPIE 2496, Detection Technologies for Mines and Minelike Targets, (20 June 1995); doi: 10.1117/12.211339
Show Author Affiliations
Quentin A. Holmes, Environmental Research Institute of Michigan (United States)
Craig R. Schwartz, Environmental Research Institute of Michigan (United States)
John H. Seldin, Environmental Research Institute of Michigan (United States)
James A. Wright, Environmental Research Institute of Michigan (United States)
Lester J. Witter, Environmental Research Institute of Michigan (United States)


Published in SPIE Proceedings Vol. 2496:
Detection Technologies for Mines and Minelike Targets
Abinash C. Dubey; Ivan Cindrich; James M. Ralston; Kelly A. Rigano, Editor(s)

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