
Proceedings Paper
Spectral fringe-adjusted joint transform correlation based efficient object classification in hyperspectral imageryFormat | Member Price | Non-Member Price |
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Paper Abstract
The spectral fringe-adjusted joint transform correlation (SFJTC) has been used effectively for
performing deterministic target detection in hyperspectral imagery. However, experiments show
decreased performance when noise-corrupted spectral variability is present in the target
signatures. In this paper, we propose to use a modified spectral fringe-adjusted joint transform
correlation based target detection algorithm, which employs a new real-valued filter called the
logarithmic fringe-adjusted filter (LFAF). Furthermore, the maximum noise fraction (MNF)
technique is used for preprocessing the hyperspectral imagery, which makes the SFJTC
technique more insensitive to spectral variability in noisy environment. Test results using real
life oil spill based hyperspectral image datasets show that the proposed scheme yields better
performance compared to alternate techniques.
Paper Details
Date Published: 29 April 2013
PDF: 11 pages
Proc. SPIE 8748, Optical Pattern Recognition XXIV, 87480T (29 April 2013); doi: 10.1117/12.2018256
Published in SPIE Proceedings Vol. 8748:
Optical Pattern Recognition XXIV
David Casasent; Tien-Hsin Chao, Editor(s)
PDF: 11 pages
Proc. SPIE 8748, Optical Pattern Recognition XXIV, 87480T (29 April 2013); doi: 10.1117/12.2018256
Show Author Affiliations
Paheding Sidike, Univ. of South Alabama (United States)
Mohammad S. Alam, Univ. of South Alabama (United States)
Published in SPIE Proceedings Vol. 8748:
Optical Pattern Recognition XXIV
David Casasent; Tien-Hsin Chao, Editor(s)
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