Nonparametric feature selection and support vector machine for polarimetric SAR data classification

There are a wide range of SAR parameters that may be extracted from polarimetric SAR data. In very complex scenes like forests it is very useful to exploit the discriminative power offered by these features. Most of these features are of complex and sometimes unknown statistical properties. For this, the conventional feature selection algorithms cannot be applied. To account for this, a nonparametric separability measure was used as the evaluation function in the feature selection process. The measure is defined as the determinant of the between-class scatter matrix to the determinant of the sum of within-class scatter matrices. To improve the classification accuracy, the method was also used in the context of a class-based feature selection. The approach, first selects a feature subset for each class, train a SVM classifier on each selected feature subset and finally combines the outputs of the classifiers in a combination scheme. The Wishart classification algorithm is used as the reference method. Experimental results using Radarsat-2 data indicate that using the developed method improves the classification accuracy.