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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 1120 of 113 papers

TitleStatusHype
Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral UnmixingCode0
Hyperspectral Unmixing Using a Neural Network AutoencoderCode0
EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral UnmixingCode0
Improved Deep Spectral Convolution Network For Hyperspectral Unmixing With Multinomial Mixture Kernel and Endmember UncertaintyCode0
Hyperspectral Blind Unmixing using a Double Deep Image PriorCode0
Dual Simplex Volume Maximization for Simplex-Structured Matrix FactorizationCode0
Dynamical Hyperspectral Unmixing with Variational Recurrent Neural NetworksCode0
Block-Simultaneous Direction Method of Multipliers: A proximal primal-dual splitting algorithm for nonconvex problems with multiple constraintsCode0
Deep Deterministic Independent Component Analysis for Hyperspectral UnmixingCode0
Deep Spectral Convolution Network for HyperSpectral UnmixingCode0
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