基于极化干涉SAR图像的地物监督分类方法

X波段的高分辨率极化干涉合成孔径雷达（synthetic aperture radar，SAR）图像包含较强的斑点噪声，不利于地物分类等应用.针对这一问题，先使用Nonlocal滤波进行预处理，然后提取图像的极化特征和干涉特征，再使用支持向量机（support vector machine，SVM）和AdaBoost分类器对极化和干涉特征矢量进行分类.利用N-SAR系统于渭南市采集的极化干涉SAR数据进行验证，该数据共包含10类地物，并有足够的ground truth用来进行分类器的训练和测试.实验结果表明，AdaBoost分类器能对多类地物取得较好的分类效果，且干涉信息的加入能带来一定改善.

A polarimetric interferometric SAR image-based land cover supervised classfication method

High-resolution X-band polarimetric interferometric synthetic aperture radar (PolInSAR) images often contain strong speckle noise, which can be an obstacle for applications like land cover classification. To overcome this problem, we apply the Nonlocal filter to the images first. After that, polarimetric and interferometric features are extracted and then used by the support vector machine (SVM) and the AdaBoost classifier for classification. For demonstrating the effectiveness of the presented method, we test it on an PolInSAR image of Weinan collected by N-SAR. This area contains 10 cover types and there is enough ground truth for training and validation. Classification result shows that the AdaBoost classifier achieves good performance for various cover types, and that interferometric information can improve the accuracy.