基于半监督学习的SVM-Wishart极化SAR图像分类方法

滑文强 王爽 侯彪

滑文强, 王爽, 侯彪. 基于半监督学习的SVM-Wishart极化SAR图像分类方法[J]. 雷达学报, 2015, 4(1): 93-98. doi: 10.12000/JR14138
引用本文: 滑文强, 王爽, 侯彪. 基于半监督学习的SVM-Wishart极化SAR图像分类方法[J]. 雷达学报, 2015, 4(1): 93-98. doi: 10.12000/JR14138
Hua Wen-qiang, Wang Shuang, Hou Biao. Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart[J]. Journal of Radars, 2015, 4(1): 93-98. doi: 10.12000/JR14138
Citation: Hua Wen-qiang, Wang Shuang, Hou Biao. Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart[J]. Journal of Radars, 2015, 4(1): 93-98. doi: 10.12000/JR14138

基于半监督学习的SVM-Wishart极化SAR图像分类方法

doi: 10.12000/JR14138
基金项目: 

国家自然科学基金(61173092, 61271302)和陕西省科学技术研究发展计划项目(2013KJXX-64)资助课题

详细信息
    作者简介:

    滑文强(1987-),男,陕西人,西安电子科技大学博士研究生,主要研究领域为极化SAR图像处理、机器学习等。E-mail:huawenqiang2013@163.com

    王爽(1978-),女,陕西人,西安电子科技大学教授,博士生导师,智能信息处理研究所副所长,智能感知与图像理解教育部重点实验室成员,国家“111”计划创新引智基地成员,IEEE会员,IET会员,中国电子学会会员,中国计算机学会会员。主要从事SAR/POLSAR处理与分析、稀疏表示、机器学习等方面的研究工作。E-mail:shwang@mail.xidian.edu.cn

    侯彪(1974-),男,陕西人,西安电子科技大学教授,博士生导师,智能感知与图像理解教育部重点实验室副主任,IEEE会员,IET西安分会执行委员会委员,中国电子学会高级会员,陕西信号处理学会理事,教育部创新团队成员。主要研究方向为遥感图像解译、压缩感知、稀疏表示等。E-mail:avcodec@163.com

Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart

  • 摘要: 该文针对极化SAR (Synthetic Aperture Radar)图像分类中的小样本问题,提出了一种新的半监督分类算法。考虑到极化SAR数据反映了地物的散射特性,该方法首先利用目标分解方法提取了多种极化散射特征;其次,在协同训练框架下结合SVM分类器构建了协同半监督模型,该模型可以同时利用有标记和无标记样本对极化SAR图像进行分类,从而在小样本时可以获得更好的分类精度;最后,为进一步改善分类结果,在协同训练分类完成后,该方法又利用Wishart分类器对分类结果进行修正。理论分析与实验表明,该算法在只有少量标记样本的情况下优于传统算法。

     

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出版历程
  • 收稿日期:  2014-11-20
  • 修回日期:  2015-02-28

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