A Comparative Study of Few Satellite Image Clustering Methods Based on Spatial Neighborhood Information

Message:
Abstract:
In order to enhance the accuracy of the image clustering, many clustering algorithms such as semi-supervised clustering, clustering ensembles and Coclustering have so far been proposed where the most effective strategy is when spatial context in clustering is considered. In this study, four clustering methods in which the spatial neighborhoodinformation is used are investigated. Theseinclude: using texture information, object based clustering, Markov random field clustering and applying majority filter onthe clustering results. These four methodsare tested on the two synthetic and two real high resolution satellite images. The results show that using texture informationin clustering can enhance the overall accuracy of the clustering of the real images up to 10.8% relative to the simplek-means clustering. Markov random field clustering of the synthetic images shows 22.8% improvement in overall accuracy.The results show that using spatial context can enhance the overall accuracy of the satellite image clustering.
Language:
Persian
Published:
Journal of Geomatics Science and Technology, Volume:3 Issue: 4, 2014
Page:
77
https://www.magiran.com/p1319838