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The supervised relaxation operator combines the information from multiple ancillary data sources with the information from multispectral remote sensing image data and spatial context. Iterative calculation integrate information from the various sources, reaching a balance in consistency between these sources of information. The supervised relaxation operator is shown to produce substantial improvements in classification accuracy compared to the accuracy produced by the conventional maximum likelihood classifier using spectral data only. The convergence property of the supervised relaxation algorithm is also described. Improvement in classification accuracy by means of supervised relaxation comes at a high price in terms of computation. In order to overcome the computation-intensive problem, a distributed/parallel implementation is adopted to take advantage of a high degree of inherent parallelism in the algorithm.