Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing

Bilateral Filtering with Clusters by Expectation Maximization

Image restoration keeping sharp edges is achieved by bilateral filter. In this paper, an approach to improve edges for the filter is presented. The proposed algorithm relies on clustering by Expectation Maximization that produced clusters of intensive values. A stage is followed where standard deviation of Gaussian filters for scales of the spatial and intensity are adjusted by features of the ...

Blind spectral unmixing by local maximization of non-Gaussianity

We approach the estimation of material percentages per pixel (endmember fractional abundances) in hyperspectral remote-sensed images as a blind source separation problem. This task is commonly known as spectral unmixing. Classical techniques require the knowledge of the existing materials and their spectra, which is an unrealistic situation in most cases. In contrast to recently presented blind...

Unscented kalman filtering of line spectral frequencies

We propose a new method for estimating Line Spectral Frequency (LSF) trajectories that uses unscented Kalman filtering (UKF). This method is based upon an iterative Expectation Maximisation (EM) approach in which LSF estimates are generated during a forward pass and then smoothed during a backward pass. The EM approach also provides re-estimated Kalman filter parameters for further forward-back...

Expectation Maximization

Expectation Maximization

The Expectation Maximization (EM) algorithm [1, 2] is one of the most widely used algorithms in statistics. Suppose we are given some observed data X and a model family parametrized by θ, and would like to find the θ which maximizes p(X |θ), i.e. the maximum likelihood estimator. The basic idea of EM is actually quite simple: when direct maximization of p(X |θ) is complicated we can augment the...