[PDF][PDF] Nonnegative matrix factorization for signal and data analytics: Identifiability, algorithms, and applications.
X≈ WH, W∈ RM× R, H∈ RN× R,(1) to 'explain'the data matrix X, where W≥ 0, H≥ 0, and
R≤ min {M, N}. At first glance, NMF is nothing but an alternative factorization model to …
R≤ min {M, N}. At first glance, NMF is nothing but an alternative factorization model to …
Blind hyperspectral unmixing using an extended linear mixing model to address spectral variability
Spectral unmixing is one of the main research topics in hyperspectral imaging. It can be
formulated as a source separation problem, whose goal is to recover the spectral signatures …
formulated as a source separation problem, whose goal is to recover the spectral signatures …
Hyperspectral unmixing with spectral variability using a perturbed linear mixing model
PA Thouvenin, N Dobigeon… - IEEE Transactions on …, 2015 - ieeexplore.ieee.org
Given a mixed hyperspectral data set, linear unmixing aims at estimating the reference
spectral signatures composing the data-referred to as endmembers-their abundance …
spectral signatures composing the data-referred to as endmembers-their abundance …
A convex optimization-based coupled nonnegative matrix factorization algorithm for hyperspectral and multispectral data fusion
Fusing a low-spatial-resolution hyperspectral data with a high-spatial-resolution (HSR)
multispectral data has been recognized as an economical approach for obtaining HSR …
multispectral data has been recognized as an economical approach for obtaining HSR …
Linear and nonlinear unmixing in hyperspectral imaging
Mainly due to the limited spatial resolution of the data acquisition devices, hyperspectral
image pixels generally result from the mixture of several components that are present in the …
image pixels generally result from the mixture of several components that are present in the …
Exploring structured sparsity by a reweighted Laplace prior for hyperspectral compressive sensing
Hyperspectral compressive sensing (HCS) can greatly reduce the enormous cost of
hyperspectral images (HSIs) on imaging, storage, and transmission by only collecting a few …
hyperspectral images (HSIs) on imaging, storage, and transmission by only collecting a few …
Multi-task learning for blind source separation
Blind source separation (BSS) aims to discover the underlying source signals from a set of
linear mixture signals without any prior information of the mixing system, which is a …
linear mixture signals without any prior information of the mixing system, which is a …
Band-wise nonlinear unmixing for hyperspectral imagery using an extended multilinear mixing model
B Yang, B Wang - IEEE Transactions on Geoscience and …, 2018 - ieeexplore.ieee.org
Most nonlinear mixture models and unmixing methods in the literature assume implicitly that
the degrees of multiple scatterings at each band are the same. However, it is commonly …
the degrees of multiple scatterings at each band are the same. However, it is commonly …
Deep generative models for library augmentation in multiple endmember spectral mixture analysis
Multiple endmember spectral mixture analysis (MESMA) is one of the leading approaches to
perform spectral unmixing (SU) considering the variability of the endmembers (EMs). It …
perform spectral unmixing (SU) considering the variability of the endmembers (EMs). It …
Hyperspectral unmixing with robust collaborative sparse regression
Recently, sparse unmixing (SU) of hyperspectral data has received particular attention for
analyzing remote sensing images. However, most SU methods are based on the commonly …
analyzing remote sensing images. However, most SU methods are based on the commonly …