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This study has three aims: firstly, to define an efficient and accurate supervised classification method to classify land use/land cover on per‐field basis using mixture distribution models. The second aim was to demonstrate the working principle of the per‐field classification method based on mixture distribution models by classifying a Landsat Thematic Mapper selected test image of an agricultural area. The third aim was to compare the overall classification accuracy and performance of the per‐field classification method based on mixture distribution models with those of three per‐pixel classification methods: minimum distance, nearest neighbour and maximum likelihood.