A per‐field classification method based on mixture distribution models and an application to Landsat Thematic Mapper data
Authors: Erol, H.; Akdeniz, F.
Source: International Journal of Remote Sensing, Volume 26, Number 6, 2005 , pp. 1229-1244(16)
Publisher: Taylor and Francis Ltd
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
Affiliations: Çukurova University, Faculty of Arts and Sciences, Department of Statistics, 01330 Adana, Turkey, firstname.lastname@example.org, Email: email@example.com
Publication date: 2005-03-01