Pol-SAR Images Classification Using Texture Features and the Complex Wishart Distribution

被引：7
|
作者：
Zhou, Guangyi [1 ]
Cui, Yi [1 ]
Chen, Yilun [1 ]
Yin, Junjun [1 ]
Yang, Jian [1 ]
Su, Yang [2 ]
机构：
[1] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
[2] Harbin Inst Technol, Sch Elect & Informat Engn, Harbin, Peoples R China
来源：
2010 IEEE RADAR CONFERENCE | 2010年
基金：
中国国家自然科学基金;
关键词：
D O I：
10.1109/RADAR.2010.5494572
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, a new method for supervised classification of terrain types in polarimetric Synthetic Aperture Radar (Pol-SAR) images is proposed. This technique is a combination of the texture classification and the maximum likelihood classification based on the complex Wishart distribution for the polarimetric covariance matrix. The texture features are first extracted from the span image based on co-occurrence matrices; and then the classifier combines the texture features with the distance measure based on polarimetric information to obtain the results. Using a NASA/JPL AIRSAR image, the effectiveness of the proposed method is demonstrated.
引用
收藏
页码：491 / 494
页数：4
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