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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 51–60 of 113 papers

TitleStatusHype
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
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