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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 51–60 of 113 papers

TitleStatusHype
Smoothed Separable Nonnegative Matrix FactorizationCode0
Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation—0
HYPERION: Hyperspectral Penetrating-type Ellipsoidal Reconstruction for Terahertz Blind Source Separation—0
On Hyperspectral Unmixing—0
Identifiability-Guaranteed Simplex-Structured Post-Nonlinear Mixture Learning via Autoencoder—0
Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing—0
Probabilistic Simplex Component Analysis—0
SCA-Net: A Self-Correcting Two-Layer Autoencoder for Hyper-spectral Unmixing—0
A Plug-and-Play Priors Framework for Hyperspectral UnmixingCode0
Hyperspectral Unmixing via Nonnegative Matrix Factorization with Handcrafted and Learnt Priors—0
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