Statistical quality assessment criteria for a linear mixing model with elliptical t-distribution errors

The linear mixing model is widely used in hyperspectral imaging applications to model the reflectance spectra of mixed pixels. In both cases it is important to detect the presence of materials or gases and then estimate their amount, if they are present. The detection and estimation algorithms available for these tasks are related but they are not identical. The objective of this paper is to theoretically investigate how the heavy tails observed in hyperspectral background data affect the quality of abundance estimates and how the F-test, used for endmember selection, is robust to the presence of heavy tails when the model fits the data.