COMBINING FUZZY PROBABILITY AND FUZZY CLUSTERING FOR MULTISPECTRAL SATELLITE IMAGERY CLASSIFICATION

Mai Dinh Sinh, Le Hung Trinh, Ngo Thanh Long

Abstract


This paper proposes a method of combining fuzzy probability and fuzzy clustering algorithm to classify on multispectral satellite images by relying on fuzzy probability to calculate the number of clusters and the centroid of clusters then using fuzzy clustering to classifying land-cover on the satellite image. In fact, the classification algorithms, the initialization of the clusters and the initial centroid of clusters have great influence on the stability of the algorithms, dealing time and classification results; the unsupervised classification algorithms such as k-Means, c-Means, Iso-data are used quite common for many problems, but the disadvantages is the low accuracy and unstable, especially when dealing with the problems on the satellite image. Results of the algorithm which are proposed show significant reduction of noise in the clusters and comparison with various clustering algorithms like k-means, iso-data, so on.

 


Keywords


Satellite Imagery; Probability; fuzzy c-means clustering

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DOI: https://doi.org/10.15625/0866-708X/54/3/6463 Display counter: Abstract : 161 views. PDF : 126 views.

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Bioteknologi Agrikultur

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Published by Vietnam Academy of Science and Technology