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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

Showing 2130 of 113 papers

TitleStatusHype
Block-Simultaneous Direction Method of Multipliers: A proximal primal-dual splitting algorithm for nonconvex problems with multiple constraintsCode0
EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral UnmixingCode0
A Gaussian mixture model representation of endmember variability in hyperspectral unmixingCode0
Deep Spectral Convolution Network for HyperSpectral UnmixingCode0
Hyperspectral Unmixing Using a Neural Network AutoencoderCode0
Nonlinear hyperspectral unmixing with robust nonnegative matrix factorizationCode0
Dual Simplex Volume Maximization for Simplex-Structured Matrix FactorizationCode0
Dynamical Hyperspectral Unmixing with Variational Recurrent Neural NetworksCode0
Semi-NMF Regularization-Based Autoencoder Training for Hyperspectral UnmixingCode0
MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor DecompositionCode0
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