Anisotropic background models for spectral target detection

James Theiler

doi:10.1117/12.2660856

13 June 2023 Anisotropic background models for spectral target detection

James Theiler

Proceedings Volume 12519, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX ; 125190R (2023) https://doi.org/10.1117/12.2660856
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Algorithms are derived for detecting targets in cluttered backgrounds, where the background is modeled as a product of univariate distributions independently fit to each of the principal component projections. Thus, fatter-than-Gaussian tails are fit to the data, with a different fatness parameter for each principal component. Comparisons are made to elliptically-contoured distributions (which, unlike these product distributions, are isotropic in the whitened space), including the multivariate t and the Gaussian. Numerical experiments are performed on hyperspectral data from the SHARE 2012 exercise, with target detection performance evaluated on both actual and simulated targets. Both direct and residual data are considered, with the residual data obtained from local background subtraction – these residual data are found to exhibit not only lower variance, but qualitatively different tail statistics. More direct target-agnostic measures are also employed to asses how well these models fit the different kinds of background clutter.

Conference Presentation

Citation Download Citation

James Theiler "Anisotropic background models for spectral target detection", Proc. SPIE 12519, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX , 125190R (13 June 2023); https://doi.org/10.1117/12.2660856

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