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A Novel Supervised Classification Scheme based on Adaboost for Polarimetric SAR

机译:一种基于Adaboost的极化SAR新型监督分类方案

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摘要

In this paper,a novel scheme for supervised classification problem of Polarimetric SAR images is proposed,which is based on Adaboost.Compared to traditional classifiers such as complex Wishart distribution based maximum likelihood classifier or Neural Network based classifier,the proposed method is more robust and flexible.Different features or parameters extracted from Polarimetric SAR data could be adopted into the scheme and a quantitative analysis on the significance of each parameter for classification could be achieved.Experiment results demonstrated the effectiveness of the proposed scheme.
机译:本文提出了一种基于Adaboost的极化SAR图像监督分类问题的新方案。与基于复杂Wishart分布的最大似然分类器或基于神经网络的分类器等传统分类器相比,该方法具有较强的鲁棒性和鲁棒性。该方案可采用从极化SAR数据中提取的不同特征或参数,并对每个参数对分类的意义进行定量分析,实验结果证明了该方案的有效性。

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