Applying the Log-Cumulants of Texture Parameter to Fully Polarimetric SAR Classification Using Support Vector Machines Classifier

摘要：

In this paper, we proposed a fully polarimetric SAR classification method based on the log-cumulants of texture parameter of the fully polarimetric SAR data. Unlike other classification algorithms that classify pixels by their scattering characteristics, this method will use a combination of the texture parameter of fully polarimetric SAR data and the Support Vector Machines (SVM) Classifier based on the spherically invariant random vectors (SIRV) model. A full polarimetric image Oberpfaffenhofen region in Germany, acquired by E-SAR at L-band, is used for our experiment. It is shown that the proposed method is consistent with the actual scattering mechanisms, especially for urban areas, and can be used to effectively distinguish different types of terrains.

关键词： Log-Cumulants Support Vector Machines Classifier Spherically Invariant Random Vectors Model Polarimetric SAR Classification

作者: Meng Liu Hong Zhang Chao Wang

作者单位: Graduate University of the Chinese Academy of Sciences, Beijing 100049 Center for Earth Observation and Digital Earth, CAS, Beijing 100094

会议类型: 国际会议

会议名称: 2011 IEEE CIE International Conference on Radar(2011年IEEE国际雷达会议RADAR 2011)

会议地点: 成都

会议语种:英文

页码: 728-731

在线出版日期: 2011-10-24（万方平台首次上网日期，不代表论文的发表时间）