A per‐field classification method based on mixture distribution models and an application to Landsat Thematic Mapper data
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.
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Document Type: Research Article
Publication date: March 1, 2005