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Proceedings Paper

Spectrally-invariant synthetic discriminant signature for hyperspectral target detection using spectral fringe-adjusted joint transform correlation
Author(s): Aed El-Saba; Adel A. Sakla; Wesam A. Sakla
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Paper Abstract

A practical challenge that designers of hyperspectral (HS) target detection algorithms must confront is the variety of spectral sampling properties exhibited by various HS imaging sensors. Examples of these variations include different spectral resolutions and the possibility of regular or irregular sampling. To confront this problem, we propose construction of a spectral synthetic discriminant signature (SSDS). The SSDS is constructed from q spectral training signatures which are obtained by sampling the original target signature. Since the SSDS is formulated offline, it does not impose any burden on the processing speed of the recognition process. Results on our HS scenery show that use of the SSDS in conjunction with the spectral fringe-adjusted joint transform correlation (SFJTC) algorithm provides spectrallyinvariant target detection, yielding area under ROC curve (AUROC) values above 0.993.

Paper Details

Date Published: 12 May 2010
PDF: 10 pages
Proc. SPIE 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, 76961T (12 May 2010); doi: 10.1117/12.850520
Show Author Affiliations
Aed El-Saba, Univ. of South Alabama (United States)
Adel A. Sakla, Univ. of South Alabama (United States)
Wesam A. Sakla, Texas A&M Univ. (United States)


Published in SPIE Proceedings Vol. 7696:
Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI
Firooz A. Sadjadi; David P. Casasent; Steven L. Chodos; Abhijit Mahalanobis; William E. Thompson; Tien-Hsin Chao, Editor(s)

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