BMUA-N applies multiscale spatial regularization to kernel-based nonlinear spectral unmixing and derives its regularization constants from estimated noise statistics, reporting better abundance accuracy than TV-regularized and other baselines.
Generalized linear mixing model accounting for endmember variability,
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A Blind Multiscale Spatial Regularization Framework for Kernel-based Spectral Unmixing
BMUA-N applies multiscale spatial regularization to kernel-based nonlinear spectral unmixing and derives its regularization constants from estimated noise statistics, reporting better abundance accuracy than TV-regularized and other baselines.