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This paper presents an image segmentation method using contourlets for spatial transformation. Independent component analysis (ICA) is used to obtain features that are independent and uncorrelated. Feature reduction is also performed during the preprocessing stage of the ICA. A kernel- based approach for clustering the dataset will eliminate the need to calculate cross-correlation energies explicitly. Instead of performing clustering over the whole image, two stages of kernel- based clustering help in reducing the computation complexity of the segmentation method. The segmentation method is applied to LandSat 7 ETM+ imagery. Results show a promising use of the presented approach for image information mining within a semantic framework. These primitive features can subsequently used for object identification.