370 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
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
Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks
Multitemporal hyperspectral unmixing (MTHU) is a fundamental tool in the
analysis of hyperspectral image sequences. It reveals the dynamical evolution
of the materials (endmembers) and of their proportions (abundances) in a given
scene. However, adequately accounting for the spatial and temporal variability
of the endmembers in MTHU is challenging, and has not been fully addressed so
far in unsupervised frameworks. In this work, we propose an unsupervised MTHU
algorithm based on variational recurrent neural networks. First, a stochastic
model is proposed to represent both the dynamical evolution of the endmembers
and their abundances, as well as the mixing process. Moreover, a new model
based on a low-dimensional parametrization is used to represent spatial and
temporal endmember variability, significantly reducing the amount of variables
to be estimated. We propose to formulate MTHU as a Bayesian inference problem.
However, the solution to this problem does not have an analytical solution due
to the nonlinearity and non-Gaussianity of the model. Thus, we propose a
solution based on deep variational inference, in which the posterior
distribution of the estimated abundances and endmembers is represented by using
a combination of recurrent neural networks and a physically motivated model.
The parameters of the model are learned using stochastic backpropagation.
Experimental results show that the proposed method outperforms state of the art
MTHU algorithms
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