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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 91–100 of 113 papers

TitleStatusHype
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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