Abstract:

Several techniques exist for dealing with spectral variability in hyperspectral unmixing, such as multiple endmember spectral mixture analysis (MESMA) or compositional models. These algorithms are computationally very involved, and often cannot be executed on problems of reasonable size. In this work, we present a new algorithm for solving the unmixing problem when spectral variability is present. The algorithm uses a library-based approach to describe the variability present in each class, and executes an alternating optimization with respect to these libraries. The optimization problem itself is constructed as an angle minimization problem by exploiting the geometrical interpretation of the unmixing problem. This results in an algorithm which yields almost identical results as MESMA, but is computationally much more favorable.

Links:

Citation:

R. Heylen, P. Scheunders, A. Zare, and P. Gader, “Alternating Angle Minimization Based Unmixing with Endmember Variability,” in IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), 2016.

@InProceedings{heylen2016alternating,
author = {Rob Heylen and Paul Scheunders and Alina Zare and Paul Gader},
title = {Alternating Angle Minimization Based Unmixing with Endmember Variability},
booktitle = {IEEE Int. Geosci. Remote Sens. Symp. (IGARSS)},
year = {2016},
}