A New Fuzzy Unsupervised Classification Method for SAR Images
Lan Gao, Feng Pan, Xiaoquan Li · 2006
This paper is to investigate a new unsupervised approach for extracted the objects based on Synthetic Aperture Radar (SAR) image using improving fuzzy clustering method. The traditional Fuzzy c-means clustering (FCM) is very sensitive to the initial value and the number of clusters. The accurate initial value and number of clusters are important parameters to get the accurate result in FCM. SAR image has extensive application in national economy and military field. And a typical characteristic of SAR image is that it is influenced by Speckle noise. So the traditional algorithm[1] of FCM applies directly SAR image to get the ideal result difficultly. This paper employs the textural feature in SAR image to extract the transition and propose a new fuzzy unsupervised classification method for SAR images using the transition region to define the initial value and the number of cluster adaptively. The experimental results prove the efficiency and accuracy of this unsupervised method for SAR images.