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A per‐field classification method based on mixture distribution models and an application to Landsat Thematic Mapper data

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Abstract:

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.

Document Type: Research Article

DOI: https://doi.org/10.1080/01431160512331326800

Affiliations: Çukurova University, Faculty of Arts and Sciences, Department of Statistics, 01330 Adana, Turkey, akdeniz@mail.cu.edu.tr, Email: herol@mail.cu.edu.tr

Publication date: 2005-03-01

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