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- W2366728064 abstract "FCM(Fuzzy C-Means) algorithm has good clustering efficiency,therefore it is widely used in the field of image segmentation.But it has the problem of clustering center initialization.Good initial clustering centers will constrain the value function to the overall situation optimal solution rapidly.However,inappropriate initial clustering centers,not only need more iterative times,but also may possibly caused the algorithm finally restrained to the partial optimal solution.Aim to solve the problem of clustering center initialization,the paper proposes a new approach of FCM based on cloud model which is a conversion model with uncertainty between a quality concept expressed by natural language and its quantity number expression.This method in this paper combines cloud model with FCM clustering algorithm,and is applicable in the field of remote sensing image segmentation.It uses the method of cloud transformation to determine the value and number of clustering centers automatically according to the characteristics of the samples,and with less iteration times to reach the optimal solution of the overall situation.The method improves the efficiency of traditional FCM image segmentation of remote sensing image,with excellent stability and intelligence.The paper also chose three remote sensing images of TM to carry out image segmentation experiments using the methods of cloud model based FCM,and compared it with the traditional FCM method.Experimental results prove that by using our method we can reduce the iterative times and enhance the efficiency of Fuzzy C-Mean clustering algorithm." @default.
- W2366728064 created "2016-06-24" @default.
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- W2366728064 date "2008-01-01" @default.
- W2366728064 modified "2023-10-03" @default.
- W2366728064 title "Remote Sensing Image Segmentation Based on Cloud Model and FCM" @default.
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