Unsupervised Multi-level Segmentation Framework for PolSAR Data using H-Alpha features and the Combined Edge- Region based segmentation

PolSAR (Polimetric Synthetic Aperture Radar) has been shown to be a powerful source of information. As a result of using up to four measurement channels at the same time, which increases the processing depth, it offers information about the geometrical and physical characteristics of objects. However, operating the PolSAR system to its full imaging potential requires significant computing power. In this study, a framework for fully polarimetric SAR image segmentation is proposed, in which the PolSAR signal is decomposed into four components that represent the eigenvectors of the autocovariance matrix corresponding to signals and clutter. The Unsupervised segmentation framework possesses two main processing levels. First level is the data preprocessing, including mean coherency matrix calculation, speckle reduction and polarimetric feature decomposition. Second level include the initial cluster Centers estimation, and edge-region based algorithm. This is achieved by using the combined H-Alpha and (averaged Intensity) lambda features derived from the target decomposition of the PolSAR data. Finally, k-Means clustering based on the Wishart distribution is used to optimize the iterative clustering by merging the clusters with the minimum Wishart distance. The proposed framework is applied on (Flevoland and San_Francisco Bay). The images are selected to react differently with different polarization. The performance evaluation based on qualitative (Visual) and quantitative assessments. Visual assessment provides an excellent information on clarity and delineation of different classes. It is applicable for applications need an accurate statistical information. Quantitative evaluations provide more accurate results for separating different classes in the images. The proposed algorithm is compared to the traditional Cloude-Pottier classification method. The results demonstrate that the proposed algorithm accuracy reaches (88.6 %) with error (0.114), advances over the traditional Cloude-Pottier method with accuracy (84.6 %) and error (0.154).