Document Type

Conference Paper

Publication Date

2-2015

Publication Source

Proceedings of SPIE 9405, Image Processing: Machine Vision Applications VIII

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.

Inclusive pages

940502-1 to 940502-7

ISBN/ISSN

0277-786X

Document Version

Published Version

Comments

This document is provided for download in compliance with the publisher's policy on self-archiving. Permission documentation is on file.

DOI: http://dx.doi.org/10.1117/12.2076798

Publisher

Society of Photo-optical Instrumentation Engineers

Volume

9405

Place of Publication

San Francisco, CA


Share

COinS