Pattern recognition in hyperspectral imagery using one-dimensional shifted phase-encoded joint transform correlation

Abstract

Pattern recognition in hyperspectral imagery is a challenging issue because of the high false alarm rate and computation complexity. In this paper, a one-dimensional shifted phase-encoded fringe-adjusted joint transform correlation (SPFJTC) technique is developed for hyperspectral image processing system. The proposed technique processes the reference spectral signature using a random phase mask and correlates it with the spectral signature corresponding to each pixel of the unknown input hyperspectral image cube using a simple architecture. This technique generates very high discrimination between the object of interest and background clutter. Computer simulation results using real life hyperspectral imagery show that the proposed SPFJTC technique can effectively recognize the objects of interest while alleviating the effects of false alarms and other artifacts.