Generally fuzzy c-means algorithm is one proved that very well suited for remote sensing image segmentation, exhibited sensitivity to the initial guess with regard to both speed and stability. But it also showed sensitivity to noise. This paper proposes a fully automatic technique to obtain image clusters. A modified fuzzy c-means classification algorithm is used to provide a fuzzy partition. This method is less sensitive to noise as it filters the image while clustering it, which is bgsed on the consideration of the neighbors as factors the attract pixels into their cluster. The experimental results on JERS-I Synthetic Aperture Radar (SAR) image demonstrate its potential usefulness.
The purpose of cluster analysis is to partition a data set into a number of disjoint groups or clusters. The members within a cluster are more similar to each other than members from different clusters. The Fuzzy c-Means (FCM) clustering is an iterative partitioning method that produces optimal cpartitions. Since the standard FCM algorithm takes a long time to partition a large data set. Because FCM program must read the entire data set into a memory for processing. This paper presents a method to speed up the FCM algorithm by reducing the number of numeric operations performed in each iteration, while keeping the exact result as the standard algorithm. The application of this method to multispectral satellite images has been evaluated, about 40% of time saving was obtained.
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