Image segmentation and unsupervised classification are difficult problems. We propose to combine both. A clustering process is applied over segment mean values. Only large segments are considered. The clustering is composed of a mean-shift step and a hierarchical clustering step. The approach is applied on a 9-look polarimetric SAR image. Textured and non-textured image regions are considered. The K and Wishart distributions are used respectively. The obtained region groups constitute an important simplification of the image and a good initial classification map. Multiplying the class map by the image of scalar texture component produces an image almost identical to the original where speckle 'color' noise variation is filtered out.
['Jean-Marie Beaulieu', 'R. Touzi']
Classification of Polarimetric SAR Images using Radiometric and Texture Information