Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning
Sun Xun① Huang Pingping*② Tu Shangtan③ Yang Xiangli①
①(School of Electronic Information, Wuhan University, Wuhan 430072, China) ②(Radar Research Institute, Inner Mongolia University of Technology, Hohhot 010051, China) ③(Shanghai Institute of Satellite Engineering, Shanghai 200240, China)
In this paper, we propose a supervised classification algorithm for Polarimetric Synthetic Aperture Radar (PolSAR) images using multiple-feature fusion and ensemble learning.First, we extract different polarimetric features, including extended polarimetric feature space, Hoekman, Huynen, H/alpha/A, and fourcomponent scattering features of PolSAR images.Next, we randomly select two types of features each time from all feature sets to guarantee the reliability and diversity of later ensembles and use a support vector machine as the basic classifier for predicting classification results.Finally, we concatenate all prediction probabilities of basic classifiers as the final feature representation and employ the random forest method to obtain final classification results.Experimental results at the pixel and region levels show the effectiveness of the proposed algorithm.
孙 勋, 黄平平, 涂尚坦, 杨祥立. 利用多特征融合和集成学习的极化SAR图像分类[J]. 雷达学报, 2016, 5(6): 692-700.
Sun Xun, Huang Pingping, Tu Shangtan, Yang Xiangli. Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning. JOURNAL OF RADARS, 2016, 5(6): 692-700.
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2016, Vol. 5 
