Willem J. Marais

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Time series derived from the first two spectral bands of the MODerate-resolution Imaging Spectroradiometer (MODIS) land surface reflectance product can be modelled as a pair of triply (mean, phase and amplitude) modulated cosine functions. This paper proposes a meta-optimization approach for setting the parameters of the non-linear Extended Kalman Filter to(More)
—The extraction of information on land cover classes using unsupervised methods has always been of relevance to the remote sensing community. In this paper a novel criterion is proposed which extract the inherent information in an unsuper-vised fashion from a time series. The criterion is used to fit a parametric model to a time series and derive the(More)
Atmospheric lidar observations provide a unique capability to directly observe the vertical column of cloud and aerosol scattering properties. Detector and solar-background noise, however, hinder the ability of lidar systems to provide reliable backscatter and extinction cross-section estimates. Standard methods for solving this inverse problem are most(More)
This paper describes an atmospheric lidar photon-limited imaging problem in which observations are contaminated with Poisson noise. The observations are a nonlinear function of two spatially varying physical parameters. The first parameter, called the transmittance, is known to be a bounded monotonic non-increasing function. The second parameter, called the(More)
In this paper the Bias Variance Search Algorithm is proposed as an algorithm to optimize a candidate set of initial parameters for an Extended Kalman filter (EKF). The search algorithm operates on a Bias Variance Equilibrium Point criterion to determine how to set the initial parameters. The candidate set is then used by the EKF to estimate state parameters(More)
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