Iteration classification method and experiment study based on unsupervised classification of fully polarimetric SAR image

被引：0
|
作者：
Liu, Xiu-Qing [1 ,2 ]
Yang, Ru-Liang [1 ]
机构：
[1] Lab. of Microwave Imaging Technol., Inst. of Electron., Chinese Acad. of Sci., Beijing 100080, China
[2] Graduate Sch., Chinese Acad. of Sci., Beijing 100039, China
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2004年 / 32卷 / 12期
关键词：
Algorithms - Classification (of information) - Convergence of numerical methods - Feature extraction - Image processing - Iterative methods - Maximum likelihood estimation;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Based on the theory of eigen-decomposition of fully polarimetric Synthetic Aperture Radar (SAR) and maximum likelihood (ML) classifier, an unsupervised iteration classification method is proposed. It is simple, flexible with high precision. A few schemes of the iteration classification method are given. The characteristic of each scheme is carefully analyzed, according to the properties of eigen-decomposition and ML, and each scheme hold its own merit. Experiments are done on data gotten by NASA/ JPL lab near Tien Mountains, and perfect classification results are obtained. This method is robust and has high adaptability.
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页码：1982 / 1986
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