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A huge amount of various remote sensing data have been acquired and archived during recent years. Information extraction from these data is still a challenging task, for example using the data classification. We propose the Bayesian approach to image classification using information fusion from different sources of data. The method of classification is based on the three processing steps: (1) information fission by feature extraction, (2) data and dimensionality reduction by unsupervised clustering, and (3) supervised classification with information fusion. The potential of the classification method is illustrated by the examples on ERS-1/2 Tandem interferometric synthetic aperture radar data. The continuity of tandem pairs of SAR images is ensured by already started or future missions such as TerraSAR-X, TanDEM-X, and COSMO-SkyMed.