Abstract:

This paper introduces a beta compositional model as a mixing model for hyperspectral images. Endmembers are represented via beta distributions, hereafter referred to as betas, to constrain endmembers to a physically-meaningful range. Two associated spectral unmixing algorithms are described and applied to simulated and real hyperspectral imagery.

Links:

Citation:

A. Zare, P. Gader, D. Dranishnikov, and T. Glenn, “Spectral unmixing using the beta compositional model,” in 5th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013.

@InProceedings{zare2013beta,
Title = {Spectral unmixing using the beta compositional model},
Author = {Zare, Alina and Gader, Paul and Dranishnikov, Dmitri and Glenn, Taylor},
Booktitle = {5th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
Year = {2013},
Month = {June},
}