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Proceedings Paper

Statistical analysis of spectral data for vegetation detection
Author(s): Rafael Love; J. Michael Cathcart
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Paper Abstract

Identification and reduction of false alarms provide a critical component in the detection of landmines. Research at Georgia Tech over the past several years has focused on this problem through an examination of the signature characteristics of various background materials. These efforts seek to understand the physical basis and features of these signatures as an aid to the development of false target identification techniques. The investigation presented in this paper deal concentrated on the detection of foliage in long wave infrared imagery. Data collected by a hyperspectral long-wave infrared sensor provided the background signatures used in this study. These studies focused on an analysis of the statistical characteristics of both the intensity signature and derived emissivity data. Results from these studies indicate foliage signatures possess unique characteristics that can be exploited to enable detection of vegetation in LWIR images. This paper will present review of the approach and results of the statistical analysis.

Paper Details

Date Published: 18 May 2006
PDF: 11 pages
Proc. SPIE 6217, Detection and Remediation Technologies for Mines and Minelike Targets XI, 62171R (18 May 2006); doi: 10.1117/12.666482
Show Author Affiliations
Rafael Love, Georgia Institute of Technology (United States)
J. Michael Cathcart, Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 6217:
Detection and Remediation Technologies for Mines and Minelike Targets XI
J. Thomas Broach; Russell S. Harmon; John H. Holloway, Editor(s)

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