摘要
决策树模型在极化SAR数据分类中有着极大的应用价值,既能描述分类结果的极化散射机制,又能获得较好的分类精度。但在对散射机制相似的地物进行分类时,由于经典决策树模型的节点采用的是单个特征,分类精度不理想。因此,该文提出了节点采用2维特征的方法,即在特征集相同的前提下,每次取两个特征组成特征矢量用于节点,提高了经典决策树难以区分的地物的分类精度;并且利用分类结果的混淆矩阵准确定位了导致分类误差的节点,进而对节点进行有针对性的反馈调整,进一步提高了指定地物的分类精度。利用AIRSARFlevoland数据验证了该方法的有效性,并结合极化特征描述了Flevoland地区多种植被的极化散射机制。
The decision tree model has great significance in the application of polarimetric SAR data classification,whose results in many types of classification applications obtain good accuracy and are interpretable by polarimetric scattering mechanisms.In the traditional decision tree model,because one single feature is employed by the nodes of the decision tree,the accuracy of the classification result tends to be poor,especially,for applications that classify objects with similar scattering characteristics.In this paper,we propose an improved method to create a two-dimensional vector of features instead of one single feature at the decision nodes.As a result,the classification results of the new method adopting the same feature set as the traditional decision tree can achieve better accuracy.In addition,after classification,the new method may employ a confusion matrix to identify the decision node that yields a classification error,which will facilitate the objectoriented feedback adjustment of classification results,thus making it possible to improve the classification accuracy of the specified object.Our experimental results with AIRSAR-Flevoland data prove the validity of the proposed method,and we draw some useful conclusions about the scattering characteristics of several types of vegetation.
作者
邵璐熠
洪文
Shao Luyi Hong Wen(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China University of Chinese Academy of Sciences, Beijing 100190, China)
出处
《雷达学报（中英文）》
CSCD
2016年第6期681-691,共11页
Journal of Radars
基金
国家自然科学基金(61431018)~~
关键词
决策树
极化特征
2维特征空间
混淆矩阵
结果反馈调整
Decision tree
Polarimetric features
Mapping of two-dimensional feature
Confusion matrix
Adjustment of the classification results
