利用多特征融合和集成学习的极化SAR图像分类

孙勋; 黄平平; 涂尚坦; 杨祥立

doi:10.12000/JR15132

利用多特征融合和集成学习的极化SAR图像分类

DOI: 10.12000/JR15132

1.
(武汉大学电子信息学院武汉 430072)
2.
(内蒙古工业大学雷达技术研究所呼和浩特 010051)
3.
(上海卫星工程研究所上海 200240)

基金项目:

内蒙古自治区科技计划项目（20131108，20140155），国家自然科学基金（61271401，41501414），复旦大学电磁波信息科学教育部重点实验室开放基金项目（EMW201504）

详细信息

作者简介:
孙勋(1992-),男,2014年获得武汉科技大学工学学士学位,现于武汉大学电子信息学院信号处理实验室攻读硕士学位。主要研究方向为极化合成孔径雷达图像解译。E-mail:sxun@whu.edu.cn;涂尚坦(1985-),男,2012年获武汉大学工学博士学位,现任上海卫星工程研究所微波载荷主管设计师。主要研究方向为SAR系统总体设计、极化SAR图像处理与解译、机器视觉与数据挖掘。E-mail:tsttu@126.com黄平平(1978-),男,2010年获中国科学院电子学研究所博士学位,现任内蒙古自治区雷达技术与应用重点实验室主任,内蒙古工业大学雷达技术研究所所长,副教授。主要研究方向为合成孔径雷达信号处理和微波遥感应用。E-mail:cimhwangpp@163.com;杨祥立(1991-),男,2014年获得中南民族大学工学学士学位,现于武汉大学电子信息学院信号处理实验室攻读硕士学位。主要研究方向为极化合成孔径雷达图像变化检测。E-mail:xiangliyang@whu.edu.cn

通讯作者:
黄平平cimhwangpp@163.com

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出版历程
- 收稿日期: 2015-12-27
- 修回日期: 2016-04-07
- 网络出版日期: 2016-12-28

Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning

1.
(School of Electronic Information, Wuhan University, Wuhan 430072, China)
2.
(Radar Research Institute, Inner Mongolia University of Technology, Hohhot 010051, China)
3.
(Shanghai Institute of Satellite Engineering, Shanghai 200240, China)

Funds:

The Inner Mongolia Autonomous Region Science and Technology Project (20131108,20140155),TheNational Natural Science Foundation of China (61271401,41501414),The Fudan University Key Laboratory of EMWInformation Open Fund Project (EMW201504)

摘要

摘要: 该文提出了一种利用多特征融合和集成学习的极化SAR图像监督分类算法。该算法首先提取极化SAR图像的多重特征，包括EPFS特征，Hoekman分解特征，Huynen分解特征，H/alpha/A分解特征以及扩展四分量分解特征。为保证集成学习中基本分类器的差异性与准确性，算法从5组特征集中每次随机选取两组不同的特征进行串联融合，作为SVM分类器的输入。最后，利用随机森林学习算法将所有基本分类器的预测概率集成输出最终分类结果。像素级和区域级的分类实验表明了该文算法的有效性。
- 极化合成孔径雷达 /
- 集成学习 /
- 监督分类
Abstract: In this paper, we propose a supervised classification algorithm for Polarimetric Synthetic Aperture Radar (PolSAR) images using multiple-feature fusion and ensemble learning.First, we extract different polarimetric features, including extended polarimetric feature space, Hoekman, Huynen, H/alpha/A, and fourcomponent scattering features of PolSAR images.Next, we randomly select two types of features each time from all feature sets to guarantee the reliability and diversity of later ensembles and use a support vector machine as the basic classifier for predicting classification results.Finally, we concatenate all prediction probabilities of basic classifiers as the final feature representation and employ the random forest method to obtain final classification results.Experimental results at the pixel and region levels show the effectiveness of the proposed algorithm.
- Polarimetric Synthetic Aperture Radar(PolSAR) /
- Ensemble learning /
- Supervised classification

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参考文献(21)

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