Unsupervised classification of polarimetric synthetic aperture radar images using kernel fuzzy C-means clustering

Abstract

In this article, an unsupervised clustering approach, known as the kernel fuzzy C-means (KFCM), which is based on Cloude's decomposition, is used for polarimetric synthetic aperture radar (POLSAR) image classification. The KFCM algorithm is an improved version of the fuzzy C-means (FCM) algorithm. It replaces the original Euclidean distance measure by the kernel-induced distance. The original sample space, coherency matrix T, is mapped to a higher-dimensional feature space so as to simplify the complex POLSAR data by using the Gaussian kernel function. The method has three main steps: first, the eight initial centres are obtained by averaging the coherency matrix within each partition according to the classical H/α plane so as to preserve the polarimetric property reasonably well; second, the related parameters of the KFCM algorithm are iteratively refined and third, the membership matrix is defuzzified by using the maximum membership decision rule. The distance measure used in the algorithm is derived from the complex Wishart distribution of the pixel data presented in the coherence data. The KFCM method not only takes advantage of the polarimetric scattering properties but also utilises the kernel method to cluster the nonlinear structure data with noise. The feasibility of this approach was tested by using two JPL/AIRSAR polarimetric SAR images. The experimental results showed that the KFCM clustering algorithm was better than the FCM clustering algorithm at classifying the POLSAR images.