Conventional classification algorithms makes the use of only multispectral information in remote sensing image classification. Wavelet provides spatial and spectral characteristics of a pixel along with its neighbours and hence this can be utilized for an improved classification. The major disadvantage of wavelet transform is the non availability of spatial frequency features in its directional components. The contourlet transform based laplacian pyramid followed by directional filter banks is an efficient way of extracting features in the directional components. In this paper different contourlet frame based feature extraction techniques for remote sensing images are proposed. Principal component analysis (PCA) method is used to reduce the number of features. Gaussian Kernel fuzzy C-means classifiers uses these features to improve the classification accuracy. Accuracy assessment based on field visit data and cluster validity measures are used to measure the accuracy of the classified data. The experimental result shows that the overall accuracy is improved to 1.73 % (for LISS-II), 1.81 % (for LISS-III) and 1.95 % (for LISS-IV) and the kappa coefficient is improved to 0.933 (for LISS-II), 0.0103 (for LISS-III) and 0.0214 (for LISS-IV) and also the cluster validity measures gives better results when compared to existing method

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