computer-vision-11
Hyperspectral Unmixing
3260 papers • 126 benchmarks • 313 datasets
Hyperspectral Unmixing is a procedure that decomposes the measured pixel spectrum of hyperspectral data into a collection of constituent spectral signatures (or endmembers) and a set of corresponding fractional abundances. Hyperspectral Unmixing techniques have been widely used for a variety of applications, such as mineral mapping and land-cover change detection. Source: An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing
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MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition
Lucas Drumetz, Mohamad Jouni, M. Mura, Pierre Comon•Thu Oct 05 2023
An interpretable methodological framework for low-rank multifeature hyperspectral unmixing based on tensor decomposition (MultiHU-TD) that incorporates the abundance sum-to-one constraint in the alternating optimization alternating direction method of multipliers (ADMM) algorithm and provide in-depth mathematical, physical, and graphical interpretation and connections with the extended linear mixing model.