Publications

Recently, Support Vector Machines (SVMs) have been introduced as a promising tool for performing supervised classification. This approach has been applied in different contexts and applications, such as data mining, regression analysis, and the classification of remotely sensed data. The advantage of SVMs for data classification is their ability to be used as an efficient algorithm for nonlinear classification problems, particularly in the case of extracting feature vectors from fully polarimetric SAR data. In this research, a classification algorithm based on the SVMs technique is applied to the fully polarimetric AIRSAR L-band data from the San Francisco Bay area, with a spatial resolution of 10 m. Several parameters are extracted from SAR data, including the individual channel backscatter value, Pauli decomposition coefficients, Krogager decomposition coefficients, and eigenvector decomposition parameters. Different combinations of polarimetric parameters are considered to assess the accuracy of the classification results. The accuracy of the SVMs is then compared with that obtained from several conventional classifiers, including the Maximum Likelihood classifier, Minimum Distance classifier, Mahalanobis Distance classifier, and Wishart classifier. The accuracy analysis shows that, for classification of fully polarimetric data, SVMs perform more poorly than the Wishart classifier by approximately 16%, whereas they perform better than the Maximum Likelihood, Minimum Distance, and Mahalanobis Distance classifiers by approximately 4%, 17% and 14%, respectively. Moreover, the highest accuracy is achieved by using the coefficients of Krogager decomposition in the classification procedure. This evaluation demonstrates that the SVM classifier can be used as an effective method for analyzing fully polarimetric SAR images with acceptable levels of accuracy.

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Shah Hosseini, R.; Entezari, I.; Homayouni, S.; Motagh, M.; Mansouri, B. (2011): Classification of polarimetric SAR images using Support Vector Machines. Canadian Journal of Remote Sensing, 37, 2, 220-233.