This paper focuses on the classification of multichannel images. The proposed supervised Bayesian classification
method applied to histological (medical) optical images and to remote sensing (optical and synthetic aperture
radar) imagery consists of two steps. The first step introduces the joint statistical modeling of the coregistered
input images. For each class and each input channel, the class-conditional marginal probability density functions
are estimated by finite mixtures of well-chosen parametric families. For optical imagery, the normal distribution
is a well-known model. For radar imagery, we have selected generalized gamma, log-normal, Nakagami and
Weibull distributions. Next, the multivariate d-dimensional Clayton copula, where d can be interpreted as the
number of input channels, is applied to estimate multivariate joint class-conditional statistics. As a second step,
we plug the estimated joint probability density functions into a hierarchical Markovian model based on a quadtree
structure. Multiscale features are extracted by discrete wavelet transforms, or by using input multiresolution
data. To obtain the classification map, we integrate an exact estimator of the marginal posterior mode.
This paper addresses the problem of the classification of very high resolution (VHR) SAR amplitude images of
urban areas. The proposed supervised method combines a finite mixture technique to estimate class-conditional
probability density functions, Bayesian classification, and Markov random fields (MRFs). Textural features, such
as those extracted by the greylevel co-occurrency method, are also integrated in the technique, as they allow
to improve the discrimination of urban areas. Copulas are applied to estimate bivariate joint class-conditional
statistics, merging the marginal distributions of both textural and SAR amplitude features. The resulting joint
distribution estimates are plugged into a hidden MRF model, endowed with a modified Metropolis dynamics
scheme for energy minimization. Experimental results with COSMO-SkyMed and TerraSAR-X images point out
the accuracy of the proposed method, also as compared with previous contextual classifiers.
In this paper we develop a novel classification approach for high and very high resolution polarimetric synthetic
aperture radar (SAR) amplitude images. This approach combines the Markov random field model to Bayesian
image classification and a finite mixture technique for probability density function estimation. The finite mixture
modeling is done via a recently proposed dictionary-based stochastic expectation maximization approach for
SAR amplitude probability density function estimation. For modeling the joint distribution from marginals
corresponding to single polarimetric channels we employ copulas. The accuracy of the developed semiautomatic
supervised algorithm is validated in the application of wet soil classification on several high resolution SAR
images acquired by TerraSAR-X and COSMO-SkyMed.
In the context of remotely sensed data analysis, a crucial problem is represented by the need to develop accurate
models for the statistics of pixel intensities. In this work, we develop a parametric finite mixture model for
the statistics of pixel intensities in high resolution synthetic aperture radar (SAR) images. This method is
an extension of previously existing method for lower resolution images. The method integrates the stochastic
expectation maximization (SEM) scheme and the method of log-cumulants (MoLC) with an automatic technique
to select, for each mixture component, an optimal parametric model taken from a predefined dictionary of
parametric probability density functions (pdf). The proposed dictionary consists of eight state-of-the-art SAR-specific
pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root,
Fisher and generalized Gamma. The designed scheme is endowed with the novel initialization procedure and
the algorithm to automatically estimate the optimal number of mixture components. The experimental results
with a set of several high resolution COSMO-SkyMed images demonstrate the high accuracy of the designed
algorithm, both from the viewpoint of a visual comparison of the histograms, and from the viewpoint of
quantitive accuracy measures such as correlation coefficient (above 99,5%). The method proves to be effective
on all the considered images, remaining accurate for multimodal and highly heterogeneous scenes.
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