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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 101–113 of 113 papers

TitleStatusHype
A graph Laplacian regularization for hyperspectral data unmixing—0
Self-Dictionary Sparse Regression for Hyperspectral Unmixing: Greedy Pursuit and Pure Pixel Search are Related—0
Effective Spectral Unmixing via Robust Representation and Learning-based Sparsity—0
Spectral Unmixing of Hyperspectral Imagery using Multilayer NMFCode0
Identifiability of the Simplex Volume Minimization Criterion for Blind Hyperspectral Unmixing: The No Pure-Pixel Case—0
Enhancing Pure-Pixel Identification Performance via Preconditioning—0
Structured Sparse Method for Hyperspectral Unmixing—0
Spectral Unmixing via Data-guided Sparsity—0
Nonlinear hyperspectral unmixing with robust nonnegative matrix factorizationCode0
Nonlinear unmixing of hyperspectral images using a semiparametric model and spatial regularization—0
Semidefinite Programming Based Preconditioning for More Robust Near-Separable Nonnegative Matrix Factorization—0
Robust Hyperspectral Unmixing with Correntropy based Metric—0
Fast and Robust Recursive Algorithms for Separable Nonnegative Matrix Factorization—0
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