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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 31–40 of 113 papers

TitleStatusHype
A laboratory-created dataset with ground-truth for hyperspectral unmixing evaluation—0
Enhancing Pure-Pixel Identification Performance via Preconditioning—0
Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing incorporating Endmember Independence—0
A graph Laplacian regularization for hyperspectral data unmixing—0
Extracting Optimal Solution Manifolds using Constrained Neural Optimization—0
Fast and Robust Recursive Algorithms for Separable Nonnegative Matrix Factorization—0
Fast and Structured Block-Term Tensor Decomposition For Hyperspectral Unmixing—0
GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness—0
Generalized Separable Nonnegative Matrix Factorization—0
A Dual Symmetric Gauss-Seidel Alternating Direction Method of Multipliers for Hyperspectral Sparse Unmixing—0
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