Land Cover Classification of Palsar Images by Knowledge Based Decision Tree Classifier and Supervised Classifiers Based on Sar Observables

The intent of this paper is to explore the application of information obtained from fully polarimetric data for land cover classiflcation. Various land cover classiflcation techniques are available in the literature, but still uncertainty exists in labeling various clusters to their own classes without using any a priori information. Therefore, the present work is focused on analyzing useful intrinsic information extracted from SAR observables obtained by various decomposition techniques. The eigenvalue decomposition and Pauli decomposition have been carried out to separate classes on the basis of their scattering mechanisms. The various classiflcation techniques (supervised: minimum distance, maximum likelihood, parallelepiped and unsupervised: Wishart) were applied in order to see possible difierences among SAR observables in terms of information that they contain and their usefulness in classifying particular land cover type. Another important issue is labeling the clusters, and this work is carried out by decision tree classiflcation that uses knowledge based approach. This classifler is implemented by scrupulous knowledge of data obtained by empirical evidence and their experimental validation. It has been demonstrated quantitatively that standard polarimetric parameters such as polarized backscatter coe-cients (linear, circular