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Proceedings Paper

Multiple object detection in hyperspectral imagery using spectral fringe-adjusted joint transform correlator
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Paper Abstract

Hyperspectral imaging (HSI) sensors provide plenty of spectral information to uniquely identify materials by their reflectance spectra, and this information has been effectively used for object detection and identification applications. Joint transform correlation (JTC) based object detection techniques in HSI have been proposed in the literatures, such as spectral fringe-adjusted joint transform correlation (SFJTC) and with its several improvements. However, to our knowledge, the SFJTC based techniques were designed to detect only similar patterns in hyperspectral data cube and not for dissimilar patterns. Thus, in this paper, a new deterministic object detection approach using SFJTC is proposed to perform multiple dissimilar target detection in hyperspectral imagery. In this technique, input spectral signatures from a given hyperspectral image data cube are correlated with the multiple reference signatures using the classassociative technique. To achieve better correlation output, the concept of SFJTC and the modified Fourier-plane image subtraction technique are incorporated in the multiple target detection processes. The output of this technique provides sharp and high correlation peaks for a match and negligible or no correlation peaks for a mismatch. Test results using real-life hyperspectral data cube show that the proposed algorithm can successfully detect multiple dissimilar patterns with high discrimination.

Paper Details

Date Published: 27 February 2015
PDF: 7 pages
Proc. SPIE 9405, Image Processing: Machine Vision Applications VIII, 940502 (27 February 2015); doi: 10.1117/12.2076798
Show Author Affiliations
Paheding Sidike, Univ. of Dayton (United States)
Vijayan K. Asari, Univ. of Dayton (United States)
Mohammad S. Alam, Univ. of South Alabama (United States)

Published in SPIE Proceedings Vol. 9405:
Image Processing: Machine Vision Applications VIII
Edmund Y. Lam; Kurt S. Niel, Editor(s)

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