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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 101113 of 113 papers

TitleStatusHype
Towards Robust Hyperspectral Unmixing: Mixed Noise Modeling and Image-Domain RegularizationCode0
A Gaussian mixture model representation of endmember variability in hyperspectral unmixingCode0
MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor DecompositionCode0
Semi-NMF Regularization-Based Autoencoder Training for Hyperspectral UnmixingCode0
Hyperspectral Blind Unmixing using a Double Deep Image PriorCode0
Spectral Unmixing of Hyperspectral Imagery using Multilayer NMFCode0
Deep Deterministic Independent Component Analysis for Hyperspectral UnmixingCode0
Block-Simultaneous Direction Method of Multipliers: A proximal primal-dual splitting algorithm for nonconvex problems with multiple constraintsCode0
Improved Deep Spectral Convolution Network For Hyperspectral Unmixing With Multinomial Mixture Kernel and Endmember UncertaintyCode0
Multi-Scale Convolutional Mask Network for Hyperspectral UnmixingCode0
Smoothed Separable Nonnegative Matrix FactorizationCode0
A Plug-and-Play Priors Framework for Hyperspectral UnmixingCode0
Temperature scaling unmixing framework based on convolutional autoencoderCode0
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