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With the ever-increasing number and diversity of Earth observation satellites, it steadily becomes more important be able to analyze compound data sets consisting of different types of images acquired by different sensors. In this paper, we examine different ways of obtaining joint distributions of such images, and we propose a method that enables incorporation of correlations between images while keeping a good fit to the marginal distributions. The approach basically consists of two steps. First, the marginal densities are specified. Based on this specification, each marginal variable is transformed to a normal distributed variable. The joint distribution of the transformed variables assumed to be multivariate normall Transforming back to the original scale gives a joint distribution with dependence, where the initial marginal distributions are preserved. The parameters the new joint distribution can be estimated. The focus is marginal distributions that are Gamma, K, or Gaussian, although any distribution could be considered. The joint distributions produced by the transformation method can be used in supervised classification of radar and optical images. Results obtained for set of four-look synthetic aperture radar (SAR) images, as well a combination of SAR and optical images, are presented. [ABSTRACT FROM AUTHOR]