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, cilt.26, ss.1229-1244, 2005 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 26 Konu: 6
  • Basım Tarihi: 2005
  • Doi Numarası: 10.1080/01431160512331326800
  • Dergi Adı: INTERNATIONAL JOURNAL OF REMOTE SENSING
  • Sayfa Sayısı: ss.1229-1244

Özet

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.