TAILIEUCHUNG - Combining fuzzy probability and fuzzy clustering for mulltispectral satellite imagery classification

This paper proposes a method of combining luzzy probability and fiizzy clustering algorithm to overcome these dnsadvantages. The method consists of two steps, first to calculate the number of cluster and the centroid of clusters based fuzzy probability, then to use fuzzy clustering aigoitthm to land-cover classificafion. | Journal of Science and Technology 54 (3) (2016) 300-313 DOI 10 15625/0866-708»54/3/6463 COMBINING FUZZY PROBABILITY AND FUZZY CLUSTERING FOR JVIULTISPECTRAL SATELLITE IMAGERY CLASSIFICATION Dinh-Sinh iVIai', L e - H u n g T r i n h , L o n g T h a n h N g o Le Quy Don Technical Universily. No 236 Hoang Quoc Viel Road. Bac Tu Liem . Hanoi 'Email; com Received: 23 June 2015; Accepted for publicaUon; 2 March 2016 ABSTRACT In practice, the classification algorithms and the initialization of the clusters and the initial cenUoid of clusten, have great tnfluence on the stability of the algorithtus, dealing time and classtllcation results. Some algorithms are used commonly in data classification, but their d,sadvantages are low accuracy and unstability such as it-Means algorithm, c-Means algorithm. Iso-data algorithm. Thts paper proposes a method of combining luzzy probability and fiizzy clustering algorithm to overcome these dtsadvantages. The method consists of two steps, first to calculate the number of cluster and the centroid of clusters based fuzzy probability, then to use fuzzy clustering aigoitthm to land-cover classificafion. The results showed that, the accuracy of the land cover classification using muitispecn-al satellite images according to the developed method significantly increases compared with vanous algorithms such as k-Means, Iso-data. Keyword- satelhte ituagery, probability, fuzzy c-means clustenng. I. INTRODUCTION The algorithms applied to image segmentation such as k-Means, c-Means, Iso-data show the same way based on the euchdean distance to detenmne the degree of similarity between the considered objects and cluster centroids. In problems of land cover classification, methods based on statistical parameters have been widely used because they are easy to implement and highly accurate [ 1 - 3 ] . However, these melhods are quite expensive, time consuming and unsuitable. Fuzzy logic has been widely applied in most of .

• Hoá học
• Vietnam Journal of Science and Technology
• Combining fuzzy probability
• Combining fuzzy clustering
• Mulltispectral satellite imagery classification
• Land cover classificafion
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