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Hyperspectral Unmixing

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

Papers

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Showing 71–80 of 113 papers

TitleStatusHype
Generalized Separable Nonnegative Matrix Factorization—0
Hyperspectral Unmixing via Deep Autoencoder Networks for a Generalized Linear-Mixture/Nonlinear-Fluctuation Model—0
A Dual Symmetric Gauss-Seidel Alternating Direction Method of Multipliers for Hyperspectral Sparse Unmixing—0
A laboratory-created dataset with ground-truth for hyperspectral unmixing evaluation—0
Improved Hyperspectral Unmixing With Endmember Variability Parametrized Using an Interpolated Scaling Tensor—0
Hyperspectral Unmixing Based on Clustered Multitask Networks—0
Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral VariabilityCode0
An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing—0
Improved Deep Spectral Convolution Network For Hyperspectral Unmixing With Multinomial Mixture Kernel and Endmember UncertaintyCode0
A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability—0
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