...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Deep-Learning Schemes for Full-Wave Nonlinear Inverse Scattering Problems
【24h】

Deep-Learning Schemes for Full-Wave Nonlinear Inverse Scattering Problems

机译：全波非线性逆散射问题的深度学习方案

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

This paper is devoted to solving a full-wave inverse scattering problem (ISP), which is aimed at retrieving permittivities of dielectric scatterers from the knowledge of measured scattering data. ISPs are highly nonlinear due to multiple scattering, and iterative algorithms with regularizations are often used to solve such problems. However, they are associated with heavy computational cost, and consequently, they are often time-consuming. This paper proposes the convolutional neural network (CNN) technique to solve full-wave ISPs. We introduce and compare three training schemes based on U-Net CNN, including direct inversion, backpropagation, and dominant current schemes (DCS). Several representative tests are carried out, including both synthetic and experimental data, to evaluate the performances of the proposed methods. It is demonstrated that the proposed DCS outperforms the other two schemes in terms of accuracy and is able to solve typical ISPs quickly within 1 s. The proposed deep-learning inversion scheme is promising in providing quantitative images in real time.
机译：本文致力于解决全波逆散射问题（ISP），其目的是从实测散射数据的知识中检索介电散射体的介电常数。 ISP由于多重散射而高度非线性，因此经常使用带有正则化的迭代算法来解决此类问题。但是，它们与沉重的计算成本相关联，因此，它们通常很耗时。本文提出了卷积神经网络（CNN）技术来解决全波ISP。我们介绍并比较了基于U-Net CNN的三种训练方案，包括直接反演，反向传播和主导电流方案（DCS）。进行了几个代表性的测试，包括合成数据和实验数据，以评估所提出方法的性能。事实证明，提出的DCS在准确性方面优于其他两个方案，并且能够在1 s内快速解决典型的ISP。提出的深度学习反演方案有望实时提供定量图像。

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