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A study has been carried out of 15 years of published peer-reviewed experiments on satellite image classification. The aim of the study was to assess the degree of progress being made in thematic mapping through developments in classification algorithms and also in systems approaches such as postclassification analysis, multiclassifier integration, and data fusion. The results of over 500 reported classification experiments were quantitatively analyzed. This involved examination of relationships between classification accuracy and date of publication, as well as between accuracy and various experimental parameters such as number of classes, size of feature vector, resolution of satellite data, and test area. Comparisons were also made between different types of methodology such as neural network and nonneural approaches. Overall, the results show that there has been no demonstrable improvement in classification performance over the 15-year period. The mean value of the Kappa coefficient across all experiments was found to be 0.6557 with a standard deviation of 0.1980. Expected relationships between classification accuracy and resolution and between accuracy and number of classes were also not observed in the data. Some of the implications of these findings for the future research agenda are considered.