Algorithm of target classification based on polarization synthesis is proposed, when it is acted as input value of classifier. Then, polarimetric SAR data is applied to classification experiment.
将其作为分类器的输入特征量，提出了一种基于极化合成的目标分类算法，并对实测极化SAR数据进行了分类实验。
But meanwhile, the data fusion coming from the coherence of different polarizations in the polarimetric data produces more error for lithological classification.
但是，由于不同极化状态回波信号之间的相关性，极化数据不可避免地产生数据冗余，反而增大了岩性分类的误差。
Aimed at SAR image interpretation, SVM shows good performance in image filtering, image segmentation, target discrimination and classification, as well as polarimetric SAR data classification.
针对SAR图像解译，SVM在图像滤波、图像分割、目标识别与分类、极化数据分类等过程中有很好的处理能力。
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