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摘要： 全极化SAR影像往往具有多个极化属性，不同海冰类型在不同极化方式下的成像亮度通常具有明显的差异。提出了一种适合于SAR影像海冰分类的属性差决策树分类分析(SDDT)方法，即在给定n个属性特征的样本基础上，通过计算任何两个属性的属性差特征的分类能力，选择出具有最优分类能力的属性差特征及其最优分裂值，实现海冰分类决策树的构建。采用这种策略，相当于n+C2n个属性(原始n个属性与C2n个属性差)中寻找最优分类能力的属性，不仅充分考虑了影像中原始多极化属性特征，而且增加了属性差特征的有效利用，进而提高了分类精度。另外，针对计算属性分类能力的衡量指标，在C4.5算法中提出的信息增益比GainRatio基础上，进一步考虑了分裂点的宽度ΔWidth以及分裂点属性总宽度TotalWidth，定义了分类能力指数ClassifyAbility=GainRatio*ΔWidth/TotalWidth。实验表明：采用同样的训练样本，应用SDDT算法挖掘出的海冰分类规则，比C4.5算法挖掘出的分类规则的检测精度至少提高10%以上。 关键词： SAR影像;  属性差;  决策树;  海冰分类     Abstract: Polarimetric SAR image usually has multiple polarization attributes，and the imaging brightness of different sea ice types in different polarization modes is obviously different.A decision tree (SDDT) analysis method on attributes’ subtractions suited for sea ice classification of polarimetric SAR imagery is proposed in this paper.The subtractions between any two attributes based on a given n attributes are calculated.Then their classification ability and optimal divided threshold are calculated.The most effective attribute is discovered and used to construct classification tree.According to this strategy，it equal to find the optimal subtraction attributefrom n+C 2 n features for classification，which include original n attributes and C2n  subtraction attributes.In addition，we use GainRatio to compare the classification ability between different attributes firstly.When there are several attributes with a same GainRatio，we consider the width of the split point (ΔWidth) and the total width of the attribute (TotalWidth) and define a classification ability index ClassifyAbility=GainRatio* ΔWidth/TotalWidth.By calculating and comparing ClassifyAbility index，an optimal attribute with the largest attribute classification abilitycan be selected.Experiments show that the accuracy of SDDT algorithmhas ten percent higher than that of C4.5 algorithm using a same training samples.

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