Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral Images

Sam L. Polk, Kangning Cui, Robert J. Plemmons, James M. Murphy

Abstract: Hyperspectral images encode rich structure that can be ex-ploited for material discrimination by machine learning al-gorithms. This article introduces the Active Diffusion and VCA-Assisted Image Segmentation (ADVIS) for active mate-rial discrimination. ADVIS selects high-purity, high-density pixels that are far in diffusion distance (a data-dependent met-ric) from other high-purity, high-density pixels in the hyper-spectral 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 supe-rior material discrimination in hyperspectral images.