Abstract
Pattern recognition in hyperspectral imagery is a challenging problem due to the minute nature of the target signature and the requirement to process huge amount of data. In this paper, we investigate the recent trends and advancements in joint transform correlation (JTC) based pattern recognition in hyperspectral imagery. In particular, we investigate the application of spectral fringe-adjusted JTC for efficient target recognition in hyperspectral imagery. Techniques for eliminating false target detection, minimizing effects of noise and other artifacts are considered. The performance of the spectral fringe-adjusted JTC has been compared with the existing techniques by generating ROC curves using real life hyperspectral datasets.
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