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

Dimitris G. Manolakis

doi:10.1117/12.559496

15 October 2004 Statistical quality assessment criteria for a linear mixing model with elliptical t-distribution errors

Dimitris G. Manolakis

Proceedings Volume 5546, Imaging Spectrometry X; (2004) https://doi.org/10.1117/12.559496
Event: Optical Science and Technology, the SPIE 49th Annual Meeting, 2004, Denver, Colorado, United States

Abstract

The linear mixing model is widely used in hyperspectral imaging applications to model the reflectance spectra of mixed pixels in the SWIR atmospheric window or the radiance spectra of plume gases in the LWIR atmospheric window. 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.

Citation Download Citation

Dimitris G. Manolakis "Statistical quality assessment criteria for a linear mixing model with elliptical t-distribution errors", Proc. SPIE 5546, Imaging Spectrometry X, (15 October 2004); https://doi.org/10.1117/12.559496

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