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 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 26 Issue: 6
  • Publication Date: 2005
  • Doi Number: 10.1080/01431160512331326800
  • Title of Journal : INTERNATIONAL JOURNAL OF REMOTE SENSING
  • Page Numbers: pp.1229-1244

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.