基于AdaBoost改进随机森林和SVM的极化SAR地物分类

Polarimetric SAR image classification based on AdaBoost improved random forest and SVM

为提升极化合成孔径雷达(SAR)地物分类精度,提出一种基于AdaBoost改进型随机森林和支持向量机(SVM)结合的二级分类结构。首先将AdaBoost改进型随机森林作为初级分类器,该分类器能根据决策树的分类能力赋予权重,分类能力越强则权重越高,从而提升初级分类精度。初级分类器还能评估输入特征的重要性,获得重要性排名。根据重要性排名进行特征筛选,用筛选后的特征训练SVM分类器,获取二级分类结果。最后利用邻域投票法将两级分类结果融合。AIRSAR极化数据对比实验表明,该分类结构可有效提升极化SAR地物分类精度。

In order to improve the classification accuracy of polarimetric synthetic aperture radar(SAR)images,a two-level classification structure based on AdaBoost improved random forest(RF)and support vector machine(SVM)is proposed.Firstly,the AdaBoost improved RF(ADA_RF)is taken as the first-level classifier,which can assign weights according to the classification abilities of the decision trees.ADA_RF assigns high weights to strong decision trees.The first-level classifier can also assess the importance of input features and compute a ranking list.Feature selection can be conducted according to the list.The SVM classifier is trained with the selected features to predict the second-level classification result.Finally,the neighborhood voting method is used to fuse the results.The comparison experiments of AIRSAR polarization data shows that the classification structure can effectively improve the classification accuracy of polarimetric SAR images.