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A Comparative Study of Few Satellite Image Clustering Methods Based on Spatial Neighborhood Information
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S. B. Fatemi *   , M. R. Mobasheri , A. A. Abkar |
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Abstract: (8200 Views) |
| In order to enhance the accuracy of the image clustering, many clustering algorithms such as semi-supervised clustering, clustering ensembles and Co-clustering 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 neighborhood information is used are investigated. These include: using texture information, object based clustering, Markov random field clustering and applying majority filter on the clustering results. These four methods are tested on the two synthetic and two real high resolution satellite images. The results show that using texture information in clustering can enhance the overall accuracy of the clustering of the real images up to 10.8% relative to the simple k-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 |
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| Keywords: Clustering, Remote Sensing, Satellite Image, Spatial Context |
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Full-Text [PDF 940 kb]
(2132 Downloads)
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Type of Study: Research |
Subject:
Photo&RS
Received: 2014/10/6
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