一种双频极化SAR图像分类方法

本文提出了一种利用两种不同频率下的极化SAR图像进行地物分类的新方法，该方法是基于目标的散射特性随频率变化而改变的趋势和程度实现的。基于不同频率下所提取的特征量，定义了特征变化量和特征变化平面。本论文选择了极化熵变化量 和极化度变化量 作为特征，通过将 平面分割为9个区域，进而将目标分为9个类。这种方法反映了目标散射特性随频率的变化关系，物理意义直观，实现方法简单易行。将这种分类方法与Wishart分类器相结合，就可以实现对极化SAR图像的无监督迭代分类。实测的SIR-C数据的分类结果表明，该方法是一种有效的极化SAR图像分类方法。 

Classification of 2-Frequency Polarimetric SAR Images

A new classification method of 2-frequency pol-SAR images is proposed in this paper. This method is based on that the scattering properties vary with the changing of frequencies. The feature variation parameter and feature variation plane, which can be used for targets classification, are defined in this paper. The parameters and are used in this paper and the plane of is divided into 9 different areas standing for 9 classes. This classification method is simple both to understand and to apply. Moreover, a new unsupervised classifier is developed by combining this method with the Wishart classifier. The new unsupervised classifier is applied to the measured NSAS/JPL SIR-C data, and satisfied result is obtained. As a conclusion, this classification method is effective to pol-SAR images.