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For each class, layer and quad-tree, a finite mixture model (FMM) is used for the corresponding pixelwise class-conditional pdf. This means that the function for is supposed to belong to the following class of pdfs:

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where is a pdf model depending on a vector of parameters that takes values in a parameter set , and every function is a convex linear combination of N such pdfs, parameterized by the parameter vectors and weighted by the proportions .

This modeling choice is motivated by the remarkable flexibility that FMMs offer in the characterization of data with heterogeneous statistics – a highly desirable property in the application to high spatial resolution remote sensing imagery (Hedhli et al. 2016). In the proposed methods, for each layer of each quad-tree, if the corresponding data are multispectral, then for all class-conditional pdfs, g is chosen to be a multivariate Gaussian, i.e. a Gaussian mixture model is used. In this case, the parameter vector ψn of each component obviously includes the related vector mean and covariance matrix (n = 1, 2,...,N) (Landgrebe 2003). This model is also extended to the layers populated by wavelet transforms of optical data, consistently with the linearity of the wavelet operators.

On the contrary, for each layer that is populated by SAR data, all class-conditional pdfs are modeled using FMMs in which g is a generalized Gamma distribution, i.e. generalized Gamma mixtures are used. In this case, the parameter vector θn of each n-th component includes a scale parameter and two shape parameters (n = 1, 2,..., N). The choice of the generalized Gamma mixture is explained by its accuracy in the application to high spatial resolution SAR imagery (Li et al. 2011; Krylov et al. 2013). Here, we also generalize it – albeit empirically – to the layers populated with wavelet transforms of SAR imagery.

In all of these cases, the FMM parameters are estimated through the stochastic expectation maximization (SEM) algorithm. SEM is an iterative stochastic parameter estimation technique that has been introduced for problems characterized by data incompleteness and that approaches maximum likelihood estimates under suitable assumptions (Celeux et al. 1996). It is separately applied to the training set of each class ωm in each -th layer of each k-th quad-tree, to model the corresponding class-conditional pdf In the case of the generalized Gamma mixtures for the SAR layers, it is also integrated with the method of log-cumulants (Krylov et al. 2013). Details on this combination can be found in (Moser and Serpico 2009). We recall that SEM also automatically determines the number N of mixture components, for which only an upper bound has to be provided by the operator. This upper bound was set to 10 in all of our experiments.