246 research outputs found
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Imaging spectrometers measure electromagnetic energy scattered in their
instantaneous field view in hundreds or thousands of spectral channels with
higher spectral resolution than multispectral cameras. Imaging spectrometers
are therefore often referred to as hyperspectral cameras (HSCs). Higher
spectral resolution enables material identification via spectroscopic analysis,
which facilitates countless applications that require identifying materials in
scenarios unsuitable for classical spectroscopic analysis. Due to low spatial
resolution of HSCs, microscopic material mixing, and multiple scattering,
spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus,
accurate estimation requires unmixing. Pixels are assumed to be mixtures of a
few materials, called endmembers. Unmixing involves estimating all or some of:
the number of endmembers, their spectral signatures, and their abundances at
each pixel. Unmixing is a challenging, ill-posed inverse problem because of
model inaccuracies, observation noise, environmental conditions, endmember
variability, and data set size. Researchers have devised and investigated many
models searching for robust, stable, tractable, and accurate unmixing
algorithms. This paper presents an overview of unmixing methods from the time
of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models
are first discussed. Signal-subspace, geometrical, statistical, sparsity-based,
and spatial-contextual unmixing algorithms are described. Mathematical problems
and potential solutions are described. Algorithm characteristics are
illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of
Selected Topics in Applied Earth Observations and Remote Sensin
Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery
This paper studies a fully Bayesian algorithm for endmember extraction and
abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral
image is decomposed as a linear combination of pure endmember spectra following
the linear mixing model. The estimation of the unknown endmember spectra is
conducted in a unified manner by generating the posterior distribution of
abundances and endmember parameters under a hierarchical Bayesian model. This
model assumes conjugate prior distributions for these parameters, accounts for
non-negativity and full-additivity constraints, and exploits the fact that the
endmember proportions lie on a lower dimensional simplex. A Gibbs sampler is
proposed to overcome the complexity of evaluating the resulting posterior
distribution. This sampler generates samples distributed according to the
posterior distribution and estimates the unknown parameters using these
generated samples. The accuracy of the joint Bayesian estimator is illustrated
by simulations conducted on synthetic and real AVIRIS images
Spectral Unmixing with Multiple Dictionaries
Spectral unmixing aims at recovering the spectral signatures of materials,
called endmembers, mixed in a hyperspectral or multispectral image, along with
their abundances. A typical assumption is that the image contains one pure
pixel per endmember, in which case spectral unmixing reduces to identifying
these pixels. Many fully automated methods have been proposed in recent years,
but little work has been done to allow users to select areas where pure pixels
are present manually or using a segmentation algorithm. Additionally, in a
non-blind approach, several spectral libraries may be available rather than a
single one, with a fixed number (or an upper or lower bound) of endmembers to
chose from each. In this paper, we propose a multiple-dictionary constrained
low-rank matrix approximation model that address these two problems. We propose
an algorithm to compute this model, dubbed M2PALS, and its performance is
discussed on both synthetic and real hyperspectral images
Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing
In hyperspectral images, some spectral bands suffer from low signal-to-noise
ratio due to noisy acquisition and atmospheric effects, thus requiring robust
techniques for the unmixing problem. This paper presents a robust supervised
spectral unmixing approach for hyperspectral images. The robustness is achieved
by writing the unmixing problem as the maximization of the correntropy
criterion subject to the most commonly used constraints. Two unmixing problems
are derived: the first problem considers the fully-constrained unmixing, with
both the non-negativity and sum-to-one constraints, while the second one deals
with the non-negativity and the sparsity-promoting of the abundances. The
corresponding optimization problems are solved efficiently using an alternating
direction method of multipliers (ADMM) approach. Experiments on synthetic and
real hyperspectral images validate the performance of the proposed algorithms
for different scenarios, demonstrating that the correntropy-based unmixing is
robust to outlier bands.Comment: 23 page
Distributed Unmixing of Hyperspectral Data With Sparsity Constraint
Spectral unmixing (SU) is a data processing problem in hyperspectral remote
sensing. The significant challenge in the SU problem is how to identify
endmembers and their weights, accurately. For estimation of signature and
fractional abundance matrices in a blind problem, nonnegative matrix
factorization (NMF) and its developments are used widely in the SU problem. One
of the constraints which was added to NMF is sparsity constraint that was
regularized by L 1/2 norm. In this paper, a new algorithm based on distributed
optimization has been used for spectral unmixing. In the proposed algorithm, a
network including single-node clusters has been employed. Each pixel in
hyperspectral images considered as a node in this network. The distributed
unmixing with sparsity constraint has been optimized with diffusion LMS
strategy, and then the update equations for fractional abundance and signature
matrices are obtained. Simulation results based on defined performance metrics,
illustrate advantage of the proposed algorithm in spectral unmixing of
hyperspectral data compared with other methods. The results show that the AAD
and SAD of the proposed approach are improved respectively about 6 and 27
percent toward distributed unmixing in SNR=25dB.Comment: 6 pages, conference pape
Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package
Spectral pixels are often a mixture of the pure spectra of the materials,
called endmembers, due to the low spatial resolution of hyperspectral sensors,
double scattering, and intimate mixtures of materials in the scenes. Unmixing
estimates the fractional abundances of the endmembers within the pixel.
Depending on the prior knowledge of endmembers, linear unmixing can be divided
into three main groups: supervised, semi-supervised, and unsupervised (blind)
linear unmixing. Advances in Image processing and machine learning
substantially affected unmixing. This paper provides an overview of advanced
and conventional unmixing approaches. Additionally, we draw a critical
comparison between advanced and conventional techniques from the three
categories. We compare the performance of the unmixing techniques on three
simulated and two real datasets. The experimental results reveal the advantages
of different unmixing categories for different unmixing scenarios. Moreover, we
provide an open-source Python-based package available at
https://github.com/BehnoodRasti/HySUPP to reproduce the results
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