Graph-Based Active Learning for Nearly Blind Hyperspectral Unmixing
This work formulates nearly blind hyperspectral unmixing as a semi-supervised problem: instead of assuming all endmember spectra are known, it requests abundance information or simple one-hot pseudo-labels for only a very small set of pixels selected by graph-based active learning.
Sep 11, 2023
