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


Erol H., Akdeniz F.

INTERNATIONAL JOURNAL OF REMOTE SENSING, vol.26, no.6, pp.1229-1244, 2005 (SCI-Expanded) identifier identifier

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.