17,823 research outputs found
Locating and quantifying gas emission sources using remotely obtained concentration data
We describe a method for detecting, locating and quantifying sources of gas
emissions to the atmosphere using remotely obtained gas concentration data; the
method is applicable to gases of environmental concern. We demonstrate its
performance using methane data collected from aircraft. Atmospheric point
concentration measurements are modelled as the sum of a spatially and
temporally smooth atmospheric background concentration, augmented by
concentrations due to local sources. We model source emission rates with a
Gaussian mixture model and use a Markov random field to represent the
atmospheric background concentration component of the measurements. A Gaussian
plume atmospheric eddy dispersion model represents gas dispersion between
sources and measurement locations. Initial point estimates of background
concentrations and source emission rates are obtained using mixed L2-L1
optimisation over a discretised grid of potential source locations. Subsequent
reversible jump Markov chain Monte Carlo inference provides estimated values
and uncertainties for the number, emission rates and locations of sources
unconstrained by a grid. Source area, atmospheric background concentrations and
other model parameters are also estimated. We investigate the performance of
the approach first using a synthetic problem, then apply the method to real
data collected from an aircraft flying over: a 1600 km^2 area containing two
landfills, then a 225 km^2 area containing a gas flare stack
Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery
This paper proposes a hierarchical Bayesian model that can be used for semi-supervised hyperspectral image unmixing. The model assumes that the pixel reflectances result from linear combinations of pure component spectra contaminated by an additive Gaussian noise. The abundance parameters appearing in this model satisfy positivity and additivity constraints. These constraints are naturally expressed in a Bayesian context by using appropriate abundance prior distributions. The posterior distributions of the unknown model parameters are then derived. A Gibbs sampler allows one to draw samples distributed according to the posteriors of interest and to estimate the unknown abundances. An extension of the algorithm is finally studied for mixtures with unknown numbers of spectral components belonging to a know library. The performance of the different unmixing strategies is evaluated via simulations conducted on synthetic and real data
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