@inproceedings {1986,
	title = {Fractional abundance estimation of mixed and compound materials by hyperspectral imaging.},
	booktitle = {10th Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), Sept 2019},
	year = {2019},
	pages = {pp. 1-5},
	address = {Amsterdam, Netherlands},
	abstract = {The mechanical and chemical properties of a compound material are determined by the fractional abundances of its components. In this work, we present a spectral unmixing technique to estimate the fractional abundances of the components of mixed and compound materials from hyperspectral images. The estimation of fractional abundances in mixed materials faces the main challenge of intimate mixing. In compound materials, the mixing with water causes changes in chemical properties resulting in spectral variability and non-linearity. To address these challenges, a supervised method is proposed that learns a mapping from the hyperspectral data to spectra that follow the linear mixing model. Then, a linear unmixing technique is applied on the mapped spectra to estimate the fractional abundances. To demonstrate the potential of the proposed method, experiments are conducted on hyperspectral images from mixtures of red and yellow clay powders and hardened mortar samples with varying water to cement ratios.},
	doi = {doi: 10.1109/WHISPERS.2019.8920893},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=8920893\&isnumber=8920817},
	author = {Bikram Koirala and Zohreh Zahiri and Mahdi Khodadadzadeh and Paul Scheunders}
}
@article {1987,
	title = {A supervised method for nonlinear hyperspectral unmixing},
	journal = {Remote Sensing},
	volume = {11},
	year = {2019},
	month = {10/2019},
	abstract = {Due to the complex interaction of light with the Earth surface, reflectance spectra can be described as highly nonlinear mixtures of the reflectances of the material constituents occuring in a given resolution cell of hyperspectral data. Our aim is to estimate the fractional abundance maps of the materials from the nonlinear hyperspectral data. The main disadvantage of using nonlinear mixing models is that the model parameters are not properly interpretable in terms of fractional abundances. Moreover, not all spectra of a hyperspectral dataset necessarily follow the same particular mixing model. In this work, we present a supervised method for nonlinear spectral unmixing. The method learns a mapping from a true hyperspectral dataset to corresponding linear spectra, composed of the same fractional abundances. A simple linear unmixing then reveals the fractional abundances. To learn this mapping, ground truth information is required, in the form of actual spectra and corresponding fractional abundances, along with spectra of the pure materials, obtained from a spectral library or available in the dataset. Three methods are presented for learning the nonlinear mapping, based on gaussian processes, kernel ridge regression, and feedforward neural networks. Experimental results conducted on an artificial dataset, a data set obtained by ray tracing, and a drill core hyperspectral dataset show that this novel methodology is very promising.},
	doi = {https://doi.org/10.3390/rs11202458},
	url = {https://www.mdpi.com/2072-4292/11/20/2458},
	author = {Bikram Koirala and Mahdi Khodadadzadeh and Cecilia Contreras and Zohreh Zahiri and Richard Gloaguen and Paul Scheunders}
}
