A sparse Dirichlet prior is proposed for estimating the abundance vector of hyperspectral images with a nonlinear mixing model. This sparse prior is led to an unmixing procedure in a semi-supervised scenario in which exact materials are unknown. The nonlinear model is a polynomial post-nonlinear mixing model that represents each hyperspectral pixel as a nonlinear function of pure spectral signatures corrupted by additive white noise. Simulation results show more than 50% improvement in the estimation error.
@article{arxiv.1803.02670,
title = {Bayesian Unmixing using Sparse Dirichlet Prior with Polynomial Post-nonlinear Mixing Model},
author = {Fahime Amiri and Mohammad Hossein Kahaei},
journal= {arXiv preprint arXiv:1803.02670},
year = {2018}
}
Comments
2 pages, 3 figures". arXiv admin note: substantial text overlap with arXiv:1803.00873