A review of nonlinear hyperspectral unmixing methods
In hyperspectral unmixing, the prevalent model used is the linear mixing model, and a large
variety of techniques based on this model has been proposed to obtain endmembers and …
variety of techniques based on this model has been proposed to obtain endmembers and …
Nonlinear unmixing of hyperspectral images: Models and algorithms
When considering the problem of unmixing hyperspectral images, most of the literature in
the geoscience and image processing areas relies on the widely used linear mixing model …
the geoscience and image processing areas relies on the widely used linear mixing model …
Using low-rank representation of abundance maps and nonnegative tensor factorization for hyperspectral nonlinear unmixing
Tensor-based methods have been widely studied to attack inverse problems in
hyperspectral imaging since a hyperspectral image (HSI) cube can be naturally represented …
hyperspectral imaging since a hyperspectral image (HSI) cube can be naturally represented …
Supervised nonlinear spectral unmixing using a postnonlinear mixing model for hyperspectral imagery
This paper presents a nonlinear mixing model for hyperspectral image unmixing. The
proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral …
proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral …
Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization
We introduce a robust mixing model to describe hyperspectral data resulting from the
mixture of several pure spectral signatures. The new model extends the commonly used …
mixture of several pure spectral signatures. The new model extends the commonly used …
Hyperspectral unmixing for additive nonlinear models with a 3-D-CNN autoencoder network
Spectral unmixing is an important task in hyperspectral image processing for separating the
mixed spectral data pertaining to various materials observed aiming at analyzing the …
mixed spectral data pertaining to various materials observed aiming at analyzing the …
Spatial validation of spectral unmixing results: A systematic review
RM Cavalli - Remote Sensing, 2023 - mdpi.com
The pixels of remote images often contain more than one distinct material (mixed pixels),
and so their spectra are characterized by a mixture of spectral signals. Since 1971, a shared …
and so their spectra are characterized by a mixture of spectral signals. Since 1971, a shared …
LSTM-DNN based autoencoder network for nonlinear hyperspectral image unmixing
Blind hyperspectral unmixing is an important technique in hyperspectral image analysis,
aiming at estimating endmembers and their respective fractional abundances. Consider the …
aiming at estimating endmembers and their respective fractional abundances. Consider the …
Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders
Raman spectroscopy is widely used across scientific domains to characterize the chemical
composition of samples in a nondestructive, label-free manner. Many applications entail the …
composition of samples in a nondestructive, label-free manner. Many applications entail the …
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 …