文献类型：期刊文献

中文题名：全极化SAR数据的最大后验概率分类

英文题名：Classification of Full-polarimetric Synthetic Aperture Radar Data with Maximum a Posteriori

作者：梁志锋[1] 凌飞龙[1] 陈尔学[2]

第一作者：梁志锋

通信作者：Liang, Z.

机构：[1]福州大学空间信息工程研究中心;[2]中国林业科学研究院资源信息研究所

年份：2013

卷号：38

期号：6

起止页码：648-651

中文期刊名：武汉大学学报：信息科学版

外文期刊名：Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University

收录：CSTPCD;;EI(收录号：20132616448687);Scopus(收录号：2-s2.0-84879153228);北大核心:【北大核心2011】；CSCD:【CSCD2013_2014】；

基金：国家青年科学基金资助项目(41101381);福建省科技计划资助项目(200910014);中欧"龙计划"合作项目(5314)

语种：中文

中文关键词：分类;SAR;极化;后验概率

外文关键词：classification; SAR; radar polarimetry posteriori

分类号：P237.3

摘要：结合后验概率对分类的影响和全极化SAR数据特点,提出了一种全极化SAR数据分类方法。首先将全极化SAR数据的协方差矩阵转换为9个服从正态分布的强度量;然后通过迭代分类计算类别出现的概率,对9个强度量进行基于最大后验概率的分类。以黑龙江省逊克县境内的一景ALOS PALSAR全极化数据为例,用该方法进行分类,总体精度和Kappa系数分别达到81.34%和0.84,优于传统的最大似然分类方法。
Considering the influence of the posterior and the statistic distributions of full-po- larimetric SAR data, we proposed a new classification method of full polarimetric SAR data. First, the covariance matrix of polarization SAR data was converted to nine intensity quanti- ties with normal distribution. Then, the probability of occurance for each class was calculat- ed with iterative initial classification. Finally, the nine intensity images were classified with max- imum likelihood classification method taking the probabilities of occurance for the classes into ac- count. We applied the developed method to the ALOS PALSAR full-polarimetric data of Xunke County, Heilongjiang Province. The overall accuracy is 81.34% and the Kappa coefficient 0. 84. The developed method showed higher accuracy than that from the traditional maximum likelihood classifier. This indicates that our method can improve the accuracy of classification.