Land Cover Classification of Palsar Images by Knowledge Based Decision Tree Classi- Fier 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 classification. Various land cover classification 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 classification techniques (supervised: minimum distance, maximum likelihood, parallelepiped and unsupervised: Wishart) were applied in order to see possible differences 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 classification that uses knowledge based approach. This classifier 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 coefficients (linear, circular Received 14 January 2011, Accepted 13 April 2011, Scheduled 29 April 2011 Corresponding author: Dharmendra Singh ([email protected]). 48 Mishra, Singh, and Yamaguchi and linear 45◦), co and cross-pol ratios for both linear and circular polarizations can be used as information bearing features for making decision boundaries. This forms the basis of discrimination between various classes in sequential format. The classification approach has been evaluated for fully polarimetric ALOS PALSAR L-band level 1.1 data. The classifier uses these data to classify individual pixel into one of the five categories: water, tall vegetation, short vegetation, urban and bare soil surface. The quantitative results shown by this classifier give classification accuracy of about 86%, which is better than other classification techniques.