Polarimetric SAR Image Classification Using Watershed-Transformation and Support Vector Machine

Considering the properties of watershed-transformation and support vector machine,a method for classifying polarimetric SAR image is proposed in this paper.First,polarimetric SAR image is segmented into a series of homogenous regions through watershed transformation and region merging process.Then,region-based classification is performed by utilizing support vector machine after feature extraction and sample selection.Experimental results show that the proposed classification method depresses speckle effectively,when in comparison with traditional pixel-based SVM algorithm,the classification accuracy is improved by dramatically and more interpretable result can also be achieved.