中文摘要：

为了充分利用极化合成孔径雷达（synthetic apeture radar,SAR）图像丰富的地物信息并解决单一特征在图像分类中的局限性问题,提出了一种基于特征选择双层支持向量机（support vector machine,SVM）的特征融合算法,充分利用特征间的完备性和互异性,以形成更有效的特征组合,并用于SAR图像的分类。首先,对SAR图像进行多种类型特征矢量的提取以能完整地描述全极化SAR图像;其次,进行特征归一化处理,以保证不同的特征向量在同一准则下进行选择,以期在进行分类时具有相同的作用;再次,引入空间金字塔（spatial pyramid,SP）分块提取不同尺度的特征矢量;然后,利用最小冗余最大关联（minimum redundancy and maximum relevance,mRMR）特征选择方法获取每种类别的最优特征子集,避免各类特征的简单组合导致的特征冗余和过度拟合现象;最后,引入多层的思想,构造双层SVM模型,实现单层目标类别概率的优化和再处理。实验结果验证了该算法对于极化SAR图像分类的有效性。

英文摘要：

Single type of feature vector cannot fully describe objects, in order to fully use the rich object information of polarimetric SAR images and solve this problem, this paper put forward a novel feature fusion algorithm based on feature selection and bilayer SVM for polarimetric SAR image classificationthat can make full use of the completeness and dissimilarity between the features to form a more effective feature vector. Various types of feature vectors were extracted from an original image by different methods for fully describing the PolSAR data. The feature vectors were normalized to ensure each feature vector can be selected under the same standards and have the same role in classification. A spatial pyramid is introduced to get the feature vector in different size or spatial location. A mRMR feature selection method was used to obtain the optimal feature subset for given categories to avoid redundancy and overfitting phenomenon caused by the simple combination of various feature vectors. Finally, the multilayer concept was introduced and a bilayer SVM model was constructed to optimize and reprocess the probabilities of the target category obtained by the first SVM. Experimental results on the two polarimetric SAR images achieved by the Jet Propulsion Laboratory show the superiority of the proposed approach.