  Spectral unmixing is an important and challenging problem in hyperspectral
data processing. This topic has been extensively studied and a variety of
unmixing algorithms have been proposed in the literature. However, the lack of
publicly available dataset with ground-truth makes it difficult to evaluate and
compare the performance of unmixing algorithms in a quantitative and objective
manner. Most of the existing works rely on the use of numerical synthetic data
and an intuitive inspection of the results of real data. To alleviate this
dilemma, in this study, we design several experimental scenes in our
laboratory, including printed checkerboards, mixed quartz sands, and reflection
with a vertical board. A dataset is then created by imaging these scenes with
the hyperspectral camera in our laboratory, providing 36 mixtures with more
than 130, 000 pixels with 256 wavelength bands ranging from 400nm to 1000nm.
The experimental settings are strictly controlled so that pure material
spectral signatures and material compositions are known. To the best of our
knowledge, this dataset is the first publicly available dataset created in a
systematic manner with ground-truth for spectral unmixing. Some typical linear
and nonlinear unmixing algorithms are also tested with this dataset and lead to
meaningful results.
