13 May 2010 Results of GLMM-based target detection on the RIT data set
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Abstract
The authors have recently introduced the Generalized Linear Mixing Model (GLMM), which extends the traditional Linear Mixing Model by generalizing the concept of an endmember vector to an endmember subspace. This generalization allows us to model the spectral variability that is present in a given class. The model also naturally includes the use of 'target spaces', which have been previously developed to model the variability of at-sensor radiance for a given library spectrum due to atmospheric and illumination uncertainty. In this paper, we apply the GLMM / target space approach to detecting targets in the recently released RIT test data set. In particular, we give a brief description of the underlying model, and then present our results of applying this model to the RIT data set.
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David Gillis, David Gillis, Emmett Ientilucci, Emmett Ientilucci, Jeffrey Bowles, Jeffrey Bowles, } "Results of GLMM-based target detection on the RIT data set", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 769523 (13 May 2010); doi: 10.1117/12.850242; https://doi.org/10.1117/12.850242
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