Pattern recognition in hyperspectral imagery using stable distribution analysis and one-dimensional fringe-adjusted joint transform correlation

S. Ochilov; S. Mercan; M. S. Alam

doi:10.1117/12.679634

30 August 2006 Pattern recognition in hyperspectral imagery using stable distribution analysis and one-dimensional fringe-adjusted joint transform correlation

S. Ochilov, S. Mercan, M. S. Alam

Author Affiliations +

Proceedings Volume 6311, Optical Information Systems IV; 63110X (2006) https://doi.org/10.1117/12.679634
Event: SPIE Optics + Photonics, 2006, San Diego, California, United States

Abstract

In hyperspectral imaging applications, the background generally exhibits a clearly non-Gaussian impulsive behavior, where valuable information stays in the tail. In this paper, we proposed a new technique where the background is modeled using stable distributions for robust detection of outliers. The outliers of the distribution can be considered as potential anomalies or regions of interest (ROI). To further decrease the false alarm rate, it may be necessary to compare the ROI with the given reference using a simple method. In this paper, we applied one dimensional fringe-adjusted joint transform correlation technique, which can detect both single and multiple objects in constant time while accommodating the in-plane and out-of-plane distortions. Simulation results using real life hyperspectral image data are presented to verify the effectiveness of the proposed technique.

Citation Download Citation

S. Ochilov, S. Mercan, and M. S. Alam "Pattern recognition in hyperspectral imagery using stable distribution analysis and one-dimensional fringe-adjusted joint transform correlation", Proc. SPIE 6311, Optical Information Systems IV, 63110X (30 August 2006); https://doi.org/10.1117/12.679634

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