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摘要： 以新疆阿尔泰山南麓克兰河流域典型区为研究区，利用GF-3全极化数据进行积雪探测，提出了一种基于特征优选的积雪识别方法。首先通过极化分解获取了GF-3数据的22个极化特征，并利用随机森林方法计算各特征的重要性，构建特征优选规则生成最优特征集，然后基于最优特征集对积雪进行识别。分析特征的重要性发现，同极化后向散射系数对积雪识别的贡献比交叉极化的贡献大，面散射和体散射对积雪识别的贡献比二面角散射贡献大。将该方法与最大似然法、支持向量机、BP神经网络3种分类器的对比发现，使用最优特征集并且利用随机森林方法的积雪识别精度最高（F指数为0.86，总体精度为0.79）。结果表明：基于特征优选进行积雪识别，不仅使得积雪识别效率得到提高，而且保持精度不变甚至有所增加，证明了该方法在积雪识别中的有效性。 关键词： 积雪识别;  GF?3;  极化分解;  特征优选;  随机森林     Abstract: This study proposed a recognition method for snow cover based on feature selection using GF-3 fully polarimetric data. The study area was selected from the typical area of the Kelan River Basin in the southern piedmont of the Altai Mountains， Xinjiang Province. First， we obtained 22 polarization features of GF-3 data by polarization decomposition. The importance of each feature was calculated by using Random Forest （RF） method. Then， we designed the rules of feature selection to generate the optimal feature sets， which were used to recognize snow cover with RF method. Analyzing the importance of the features， we can find that， for snow cover recognition， the contribution of the same polarization backscattering coefficient is greater than that of the cross polarization backscattering coefficient， and the contribution of the surface scattering or volume scattering is greater than that of the dihedral angle scattering. Finally， a comparison with the Maximum Likelihood， Support Vector Machine， and BP neural network was made for testing the performance of the proposed method. It is found that the optimal feature sets using RF method to recognize snow cover have the highest accuracy （F-score is 0.86， overall accuracy is 0.79）. From the selection of classifiers and the results of features selection， the proposed method is very effective in recognition of snow cover.

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