Paper Info
Title
A Supervised Method for Nonlinear Hyperspectral Unmixing
Abstract
Due to the complex interaction of light with the Earth's surface, reflectance spectra can be described as highly nonlinear mixtures of the reflectances of the material constituents occurring 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 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 shows that this novel methodology is very promising.
Year
DOI
Venue
2019
10.3390/rs11202458
REMOTE SENSING
Keywords
DocType
Volume
hyperspectral unmixing,spectral mixing models,machine learning algorithms
Journal
11
Issue
Citations 
PageRank 
20
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Bikram Koirala102.03
Mahdi Khodadadzadeh2689.12
Cecilia Contreras300.34
Zohreh Zahiri411.73
Richard Gloaguen513332.68
Paul Scheunders61190102.87