A Multisensor Hyperspectral Benchmark Dataset For Unmixing of Intimate
  Mixtures
Bikram Koirala Behnood Rasti Zakaria Bnoulkacem Andrea de Lima Ribeiro Yuleika Madriz Erik Herrmann Arthur Gestels Thomas De Kerf Sandra Lorenz Margret Fuchs Koen Janssens Gunther Steenackers Richard Gloaguen Paul Scheunders
Optical hyperspectral cameras capture the spectral reflectance of materials.
Since many materials behave as heterogeneous intimate mixtures with which each
photon interacts differently, the relationship between spectral reflectance and
material composition is very complex. Quantitative validation of spectral
unmixing algorithms requires high-quality ground truth fractional abundance
data, which are very difficult to obtain. In this work, we generated a
comprehensive laboratory ground truth dataset of intimately mixed mineral
powders. For this, five clay powders (Kaolin, Roof clay, Red clay, mixed clay,
and Calcium hydroxide) were mixed homogeneously to prepare 325 samples of 60
binary, 150 ternary, 100 quaternary, and 15 quinary mixtures. Thirteen
different hyperspectral sensors have been used to acquire the reflectance
spectra of these mixtures in the visible, near, short, mid, and long-wavelength
infrared regions (350-15385) nm. {\color{black} Overlaps in wavelength regions
due to the operational ranges of each sensor} and variations in acquisition
conditions {\color{black} resulted in} a large amount of spectral variability.
Ground truth composition is given by construction, but to verify that the
generated samples are sufficiently homogeneous, XRD and XRF elemental analysis
is performed. We believe these data will be beneficial for validating advanced
methods for nonlinear unmixing and material composition estimation, including
studying spectral variability and training supervised unmixing approaches. The
datasets can be downloaded from the following link:
https://github.com/VisionlabUA/Multisensor_datasets.