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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 76–100 of 113 papers

TitleStatusHype
Improved Hyperspectral Unmixing With Endmember Variability Parametrized Using an Interpolated Scaling Tensor—0
Inertia-Constrained Pixel-by-Pixel Nonnegative Matrix Factorisation: a Hyperspectral Unmixing Method Dealing with Intra-class Variability—0
Investigation of unsupervised and supervised hyperspectral anomaly detection—0
Low-rank and Sparse NMF for Joint Endmembers' Number Estimation and Blind Unmixing of Hyperspectral Images—0
Matrix cofactorization for joint spatial-spectral unmixing of hyperspectral images—0
Maximum Volume Inscribed Ellipsoid: A New Simplex-Structured Matrix Factorization Framework via Facet Enumeration and Convex Optimization—0
Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing—0
Multilayer Simplex-structured Matrix Factorization for Hyperspectral Unmixing with Endmember Variability—0
Multi-Resolution Beta-Divergence NMF for Blind Spectral Unmixing—0
Multitemporal Latent Dynamical Framework for Hyperspectral Images Unmixing—0
Nonlinear Hyperspectral Unmixing based on Multilinear Mixing Model using Convolutional Autoencoders—0
Nonlinear unmixing of hyperspectral images using a semiparametric model and spatial regularization—0
Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images—0
On Hyperspectral Unmixing—0
Online Unmixing of Multitemporal Hyperspectral Images accounting for Spectral Variability—0
Orthogonal Nonnegative Tucker Decomposition—0
Dynamical Hyperspectral Unmixing with Variational Recurrent Neural NetworksCode0
Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral UnmixingCode0
Hyperspectral Unmixing Using a Neural Network AutoencoderCode0
Dual Simplex Volume Maximization for Simplex-Structured Matrix FactorizationCode0
EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral UnmixingCode0
Nonlinear hyperspectral unmixing with robust nonnegative matrix factorizationCode0
Deep Spectral Convolution Network for HyperSpectral UnmixingCode0
Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral VariabilityCode0
Regularization Parameter Selection in Minimum Volume Hyperspectral UnmixingCode0
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