A LS-SVM-based classifier with Fruit Fly Optimization Algorithm for polarimetric SAR images

A classifier based on the Least Square Support Vector Machine (LS-SVM) with Fruit Fly Optimization Algorithm (FOA) for polarimetirc Synthetic Aperture Radar (SAR) image classification is proposed in this paper. This method uses pixel-based information and region-based information as the features of land cover. The former one comes from the integration of multiple polarimetric parameters obtained by various polarimetric decomposition techniques, and the latter one is derived from the Grey Level Co-occurrence Matrix (GLCM). Kernel Principal Component Analysis (KPCA) is afterwards used to reduce the dimensionality of the multi-feature data. Additionally, this method uses LS-SVM as the classifier. Due to the fact that the classification performance is dependent on the input parameters of LS-SVM, FOA is adopted to obtain the optimized input parameters. Finally, compared with the method without using FOA and the supervised Wishart method, the classification performance of a fully polarimetric SAR image is much better by using the proposed method.