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Polarimetric SAR data contains a large amount of potential information that may be used to characterize forested scenes. However, the large number of PolSAR parameters and discriminators cannot all be used in most classification problems. Some form of feature selection will improve classification results and improve the efficiency of the system. In addition, classification of PolSAR data may be improved...
The main objective is to propose a wrapper feature selection algorithm for analyzing the polarimetric SAR data for forest mapping. The method is based on the concept of feature selection and classifier ensemble. Due to its ability to take numerous and heterogeneous features into account, the support vector machine (SVM) algorithm is used as the classifier. The limitation of SVM as the evaluation function...
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...
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