Graph-Based Active Learning for Nearly Blind Hyperspectral Unmixing

Mon, 11 Sep 2023 00:00:00 +0000

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.

Two complementary models are introduced. Graph Learning Unmixing (GLU) interprets graph Laplace class probabilities directly as abundance maps and then estimates the endmembers. Graph-Regularized Semi-supervised Unmixing (GRSU) combines the linear mixing model, nonnegativity and sum-to-one constraints, graph regularization, and supervision on the queried pixels in a joint optimization problem initialized by GLU.

Experiments on Urban, Samson, and Jasper Ridge imagery show that both exact abundances and easy-to-obtain one-hot labels substantially improve blind-unmixing baselines. With only 0.4% of pixels labeled, the proposed methods improve state-of-the-art blind approaches by 50% or more on the reported reconstruction and abundance-estimation metrics.

Graph-Based Optimization | Bohan Chen's Personal Webpage

Graph-Based Active Learning for Nearly Blind Hyperspectral Unmixing