Results of GLMM-Based Target Detection on the RIT Data Set

摘要

The authors have recently introduced the Generalized Linear Mixing Model (GLMM), which extends the traditionalLinear Mixing Model by generalizing the concept of an endmember vector to an endmember subspace. Thisgeneralization allows us to model the spectral variability that is present in a given class. The model also naturallyincludes the use of ‘target spaces', which have been previously developed to model the variability of at-sensor radiancefor 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 tothe RIT data set.