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Proceedings Paper

Canonical correlation analysis of LWIR imagery in the frequency domain
Author(s): Terry L. Nichols; John K. Thomas; Wolfgang Kober; Gregory D. Arnold; Vincent J. Velten
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Paper Abstract

This paper presents a linear system approximation for automated analysis of passive, long-wave infrared (LWIR) imagery. The approach is based on the premise that for a time varying ambient temperature field, the ratio of object surface temperature to ambient temperature is independent of amplitude and is a function only of frequency. Thus, for any given material, it is possible to compute a complex transfer function in the frequency domain with real and imaginary parts that are indicative of the material type. Transfer functions for a finite set of ordered points on a hypothesized object create an invariant set for that object. This set of variates is then concatenated with another set of variates (obtained either from the same object or a different object) to form two random complex vectors. Statistical tests of affine independence between the two random vectors is facilitated by decomposing the generalized correlation matrix into canonical form and testing the hypothesis that the sample canonical correlations are all zero for a fixed probability of false alarm (PFA). In the case of joint Gaussian distributions, the statistical test is a maximum likelihood. Results are presented using real images.

Paper Details

Date Published: 18 September 1998
PDF: 11 pages
Proc. SPIE 3371, Automatic Target Recognition VIII, (18 September 1998); doi: 10.1117/12.323863
Show Author Affiliations
Terry L. Nichols, Data Fusion Corp. (United States)
John K. Thomas, Data Fusion Corp. (United States)
Wolfgang Kober, Data Fusion Corp. (United States)
Gregory D. Arnold, Air Force Research Lab. (United States)
Vincent J. Velten, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 3371:
Automatic Target Recognition VIII
Firooz A. Sadjadi, Editor(s)

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