一种改进的全极化SAR影像面向对象分类方法

An improved object oriented classification scheme for polarimetric SAR image

面向对象分类过程,首先对图像进行分割得到对象,然后将对象进行分类,分割效果直接影响最终分类精度.针对这一问题,提出一种改进的全极化合成孔径雷达(SAR)影像面向对象分类方法,在分类时首先通过计算各对象内部像元类别比例对对象进行判断,若所有类别比例均没有达到某个阈值,则认为此对象存在分割偏差,对其进行基于像元的分类,反之则进行面向对象分类,最后整合像元级和对象级分类结果.分类算法采用改进分类器动态选择法(ICDS)对差异性较大的3个基分类器Wishart、核-KNN和Wishart-KNN进行决策级融合.以AIRSAR,EMISAR的全极化SAR影像为数据进行分类实验.结果表明:改进算法充分利用了对象级和像素级分类的优点,从而得到高精度的分类结果,该算法具有良好的应用前景.

The process of object oriented classification is to segment the image first to get different objects,and then classify the objects,the result of segmentation directly affects the accuracy of final classification.For this problem,an improved object oriented classification method for PolSAR image is proposed in this paper：firstly estimate each object by calculating the percentages of different classes of all selected pixels within the object,if all percentages within the object are below a certain threshold,it can be considered that this object has deviation on segmentation,so pixel-based classification is conducted on the object;otherwise,object oriented classification is proceeded on the object,the results of pixel-based and object oriented are combined at last.Improved classifier dynamic selection algorithm is used as the classification algorithm to fulfill the decision-level fusion on three base classifiers with huge diversity includingWishart,kernel-KNN and Wishart-KNN.PolSAR images of AIRSAR and EMISAR are used as experimental data for the classification experiments,and the results show that improved object oriented classification algorithm achieves the highest performance on accuracy,it can take full advantage of merits from pixel-base and object oriented classification algorithms and can be applied well in practice.