...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Applied Remote Sensing >Selective ensemble learning and its applications in fully polarimetric SAR image classification
【24h】

Selective ensemble learning and its applications in fully polarimetric SAR image classification

机译：完全偏振SAR图像分类中的选择性集合学习及其应用

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Polarimetric SAR (PolSAR) can comprehensively describe the differences of targets combining the polarimetric scattering power and the relative phase information among the polarimetric channels. For the traditional image classification methods, a single classifier cannot fully solve the classification problem, in which certain inevitable bias between the classification results and the actual condition exists. To improve the classification performance, ensemble learning theory has been introduced into PolSAR classification, in which a number of different learners are integrated to get the final results. However, a larger number of learners may also lead to some negative effects. The concept of selective ensemble learning (SEL) is then introduced, in which some better learners are selected from a group of individual learners according to the selection strategy and then integrated to obtain a generalized classifier. Based on this idea, a SEL-based PolSAR image classification method is proposed, in which the representative features are extracted based on the characteristics of PolSAR image meanwhile high-performing learners are selected using genetic algorithm optimization. In order to verify the effectiveness and application of the proposed SEL-based PolSAR image classification method, the tests are implemented using UAVSAR L-band PolSAR data, besides the other two newly acquired experimental data of Chinese airborne and spaceborne system. The quantitative and qualitative experimental results confirm that the proposed method has excellent performance in classification. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)
机译：Polarimetric SAR（POLSAR）可以全面地描述与偏振通道之间的偏振散射功率和相对相位信息组合的目标的差异。对于传统的图像分类方法，单个分类器无法完全解决分类问题，其中存在分类结果和实际情况之间的某些不可避免的偏差。为了提高分类绩效，已经引入了集合学习理论，以波萨尔分类，其中一些不同的学习者被整合以获得最终结果。然而，更多的学习者也可能导致一些负面影响。然后介绍了选择性集合学习（SEL）的概念，其中一些更好的学习者根据选择策略从一组个别学习者中选择，然后集成以获取广义分类器。基于该思想，提出了一种基于SEL的POLSAR图像分类方法，其中基于POLSAR图像的特征提取代表特征，同时使用遗传算法优化选择高性能学习者。为了验证所提出的基于SEL的POLSAR图像分类方法的有效性和应用，除了另外两个新获取的中国机载和星载系统的实验数据之外，使用UVSAR L波段POLSAR数据实现测试。定量和定性实验结果证实，该方法在分类中具有出色的性能。 （c）2019年光学仪表工程师协会（SPIE）

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号