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27 February 2015Multiple object detection in hyperspectral imagery using spectral fringe-adjusted joint transform correlator
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.
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Paheding Sidike, Vijayan K. Asari, Mohammad S. Alam, "Multiple object detection in hyperspectral imagery using spectral fringe-adjusted joint transform correlator," Proc. SPIE 9405, Image Processing: Machine Vision Applications VIII, 940502 (27 February 2015); https://doi.org/10.1117/12.2076798