401 research outputs found
Generalized linear mixing model accounting for endmember variability
Endmember variability is an important factor for accurately unveiling vital
information relating the pure materials and their distribution in hyperspectral
images. Recently, the extended linear mixing model (ELMM) has been proposed as
a modification of the linear mixing model (LMM) to consider endmember
variability effects resulting mainly from illumination changes. In this paper,
we further generalize the ELMM leading to a new model (GLMM) to account for
more complex spectral distortions where different wavelength intervals can be
affected unevenly. We also extend the existing methodology to jointly estimate
the variability and the abundances for the GLMM. Simulations with real and
synthetic data show that the unmixing process can benefit from the extra
flexibility introduced by the GLMM
Hyperspectral unmixing accounting for spatial correlations and endmember variability
International audienceThis paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing accounting for endmember variability. This variability is obtained by assuming that each pixel is a linear combination of random endmembers weighted by their corresponding abundances. An additive noise is also considered in the proposed model generalizing the normal compositional model. The proposed model is unsupervised since it estimates the abundances and both the mean and the covariance matrix of each endmember. A classification map indicating the class of each pixel is also obtained based on the estimated abundances. Simulations conducted on a real dataset show the potential of the proposed model in terms of unmixing performance for the analysis of 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
Hyperspectral unmixing with spectral variability using a perturbed linear mixing model
International audienceGiven a mixed hyperspectral data set, linear unmixing aims at estimating the reference spectral signatures composing the data-referred to as endmembers-their abundance fractions and their number. In practice, the identified endmembers can vary spectrally within a given image and can thus be construed as variable instances of reference endmembers. Ignoring this variability induces estimation errors that are propagated into the unmixing procedure. To address this issue, endmember variability estimation consists of estimating the reference spectral signatures from which the estimated endmembers have been derived as well as their variability with respect to these references. This paper introduces a new linear mixing model that explicitly accounts for spatial and spectral endmember variabilities. The parameters of this model can be estimated using an optimization algorithm based on the alternating direction method of multipliers. The performance of the proposed unmixing method is evaluated on synthetic and real data. A comparison with state-of-the-art algorithms designed to model and estimate endmember variability allows the interest of the proposed unmixing solution to be appreciated
Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing
The recent evolution of hyperspectral imaging technology and the
proliferation of new emerging applications presses for the processing of
multiple temporal hyperspectral images. In this work, we propose a novel
spectral unmixing (SU) strategy using physically motivated parametric endmember
representations to account for temporal spectral variability. By representing
the multitemporal mixing process using a state-space formulation, we are able
to exploit the Bayesian filtering machinery to estimate the endmember
variability coefficients. Moreover, by assuming that the temporal variability
of the abundances is small over short intervals, an efficient implementation of
the expectation maximization (EM) algorithm is employed to estimate the
abundances and the other model parameters. Simulation results indicate that the
proposed strategy outperforms state-of-the-art multitemporal SU algorithms
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