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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
Hyperspectral Unmixing Based on Clustered Multitask Networks—0
Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral VariabilityCode0
An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing—0
Improved Deep Spectral Convolution Network For Hyperspectral Unmixing With Multinomial Mixture Kernel and Endmember UncertaintyCode0
A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability—0
Deep Spectral Convolution Network for HyperSpectral UnmixingCode0
Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity—0
Hyperspectral Unmixing Using a Neural Network AutoencoderCode0
A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing—0
Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing—0
A Gaussian mixture model representation of endmember variability in hyperspectral unmixingCode0
Block-Simultaneous Direction Method of Multipliers: A proximal primal-dual splitting algorithm for nonconvex problems with multiple constraintsCode0
Hyperspectral Unmixing: Ground Truth Labeling, Datasets, Benchmark Performances and Survey—0
Maximum Volume Inscribed Ellipsoid: A New Simplex-Structured Matrix Factorization Framework via Facet Enumeration and Convex Optimization—0
EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral UnmixingCode0
Hyperspectral Unmixing with Endmember Variability using Semi-supervised Partial Membership Latent Dirichlet Allocation—0
Low-rank and Sparse NMF for Joint Endmembers' Number Estimation and Blind Unmixing of Hyperspectral Images—0
Inertia-Constrained Pixel-by-Pixel Nonnegative Matrix Factorisation: a Hyperspectral Unmixing Method Dealing with Intra-class Variability—0
Semi-Supervised Endmember Identification In Nonlinear Spectral Mixtures Via Semantic Representation—0
Hyperspectral Unmixing with Endmember Variability using Partial Membership Latent Dirichlet Allocation—0
Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing—0
Online Unmixing of Multitemporal Hyperspectral Images accounting for Spectral Variability—0
A spatial compositional model (SCM) for linear unmixing and endmember uncertainty estimation—0
Semiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatches—0
Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images—0
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