  Hyperspectral images encode rich structure that can be exploited for material
discrimination by machine learning algorithms. This article introduces the
Active Diffusion and VCA-Assisted Image Segmentation (ADVIS) for active
material discrimination. ADVIS selects high-purity, high-density pixels that
are far in diffusion distance (a data-dependent metric) from other high-purity,
high-density pixels in the hyperspectral image. The ground truth labels of
these pixels are queried and propagated to the rest of the image. The ADVIS
active learning algorithm is shown to strongly outperform its fully
unsupervised clustering algorithm counterpart, suggesting that the
incorporation of a very small number of carefully-selected ground truth labels
can result in substantially superior material discrimination in hyperspectral
images.
