利用多特征融合和集成学习的极化SAR图像分类

Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning

该文提出了一种利用多特征融合和集成学习的极化SAR图像监督分类算法。该算法首先提取极化SAR图像的多重特征,包括EPFS特征,Hoekman分解特征,Huynen分解特征,H/alpha/A分解特征以及扩展四分量分解特征。为保证集成学习中基本分类器的差异性与准确性,算法从5组特征集中每次随机选取两组不同的特征进行串联融合,作为SVM分类器的输入。最后,利用随机森林学习算法将所有基本分类器的预测概率集成输出最终分类结果。像素级和区域级的分类实验表明了该文算法的有效性。

In this paper,we propose a supervised classification algorithm for Polarimetric Synthetic Aperture Radar（Pol SAR） 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 Pol SAR 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.