A Dual-input Electromagnetic Inverse Scattering Algorithm Based on Improved U-net

Authors

  • Chun Xia Yang Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University, Shanghai 200234, China
  • Jun Jie Meng Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University, Shanghai 200234, China
  • Shuang Wei Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University, Shanghai 200234, China
  • Mei Song Tong Department of Electronic Science and Technology Tongji University, Shanghai 201804, China

DOI:

https://doi.org/10.13052/2024.ACES.J.391104

Keywords:

back propagation (BP), dual-input inversion, improved U-Net, inverse scattering

Abstract

In this paper, we propose a dual-input inversion method based on deep learning to improve the accuracy of electromagnetic imaging using the back propagation algorithm (BP). An improved U-Net network is utilized to reconstruct the scatterers. Unlike other deep learning inversion methods, we input both the scatterer distribution data from BP imaging and the scattered field data received by the antennas into the neural network for training. This approach leads to a more accurate prediction of scatterer positions and characteristics. Compared to predicting the scatterers using only the scattered field as input, adding the BP imaging results at the input provides the neural network with more information, significantly reduces the learning difficulty, minimizes errors, and enhances the quality of imaging. To address potential gradient vanishing and spatial information loss during network training, we integrate attention mechanisms and residual modules into the basic U-Net network. The former helps the network extract important relevant information under different contrast conditions, while the latter focuses on solving the problems of gradient vanishing and explosion. Simulation experiments confirm that our dual-input inversion method significantly reduces the average error, outperforming traditional single-input reconstruction methods.

Downloads

Download data is not yet available.

Author Biographies

Chun Xia Yang, Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University, Shanghai 200234, China

Chun Xia Yang received the Ph.D. degree in electronic science and technology from Tongji University, Shanghai, China, in 2017. During her doctoral studies, she also conducted research at the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA as a visiting student between 2014 and 2016. She is currently an associate professor at the Department of Communication Engineering, Shanghai Normal University, Shanghai, China. Her ongoing research interests primarily revolve around electromagnetic inverse scattering for imaging and computational electromagnetics.

Jun Jie Meng, Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University, Shanghai 200234, China

Jun Jie Meng received the B.S. degree in Communication Engineering from Shanghai Normal University, China, in 2022 and is currently pursuing his M.S. degree at the same institution. His research primarily focuses on the field of electromagnetic inverse scattering.

Shuang Wei, Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University, Shanghai 200234, China

Shuang Wei received the B.S. degree and the M.S. degree from the Huazhong University of Science and Technology, Wuhan, China, in 2005 and 2007 respectively, and the Ph.D. degree in electrical and computer engineering from the University of Calgary, Calgary Alberta, Canada, in 2011. She is now an Associate Professor with College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China. From 2021 to 2022, she was a Visiting Scholar at the Shanghai Key Laboratory of Navigation and Location-based Services, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China. Her current research interests include array signal processing, signal estimation and detection, computational intelligence, and compressed sensing.

Mei Song Tong, Department of Electronic Science and Technology Tongji University, Shanghai 201804, China

Meisong Tong received the B.S. and M.S. Degrees from Huazhong University of Science and Technology, Wuhan, China, respectively, and Ph.D. degree from Arizona State University, Tempe, Arizona, USA, all in electrical engineering. He is currently the Humboldt Professor at the Technical University of Munich, Munich, Germany; the Distinguished/Permanent Professor, Head of Department of Electronic Science and Technology, and Vice Dean of College of Microelectronics, Tongji University, Shanghai, China. He has also held an adjunct professorship at the University of Illinois at Urbana-Champaign, Urbana, Illinois, USA, and an honorary professorship at the University of Hong Kong, China. He has published more than 700 papers in refereed journals and conference proceedings and co-authored six books or book chapters. His research interests include electromagnetic field theory, antenna theory and design, simulation and design of RF/microwave circuits and devices, interconnect and packaging analysis, inverse electromagnetic scattering for imaging, and computational electromagnetics.

Prof. Tong is a Fellow of the Electromagnetics Academy, Fellow of the Japan Society for the Promotion of Science (JSPS), and Senior Member (Commission B) of the USNC/URSI. He has been the chair of Shanghai Chapter since 2014 and the chair of SIGHT committee in 2018, respectively, in IEEE Antennas and Propagation Society. He has served as an associate editor or guest editor for several well-known international journals, including IEEE Antennas and Propagation Magazine, IEEE Transactions on Antennas and Propagation, IEEE Transactions on Components, Packaging and Manufacturing Technology, International Journal of Numerical Modeling: Electronic Networks, Devices and Fields, Progress in Electromagnetics Research, and Journal of Electromagnetic Waves and Applications, etc. He also frequently served as a session organizer/chair, technical program committee member/chair, and general chair for some prestigious international conferences. He was the recipient of a Visiting Professorship Award from Kyoto University, Japan, in 2012, and from University of Hong Kong, China, 2013. He advised and coauthored 15 papers that received the Best Student Paper Award from different international conferences. He was the recipient of the Travel Fellowship Award of USNC/URSI for the 31th General Assembly and Scientific Symposium (GASS) in 2014, Advance Award of Science and Technology of Shanghai Municipal Government in 2015, Fellowship Award of JSPS in 2016, Innovation Award of Universities’ Achievements of Ministry of Education of China in 2017, Innovation Achievement Award of Industry-Academia-Research Collaboration of China in 2019, “Jinqiao” Award of Technology Market Association of China in 2020, Baosteel Education Award of China in 2021, Carl Friedrich von Siemens Research Award of the Alexander von Humboldt Foundation of Germany in 2023, and Technical Achievement Award of Applied Computational Electromagnetic Society (ACES) of USA in 2024. In 2018, he was selected as the Distinguished Lecturer (DL) of IEEE Antennas and Propagation Society for 2019-2022.

References

L. Xie, J. Huo, Y. Huang, C. Guo, and Q. Zhao, “Non-iterative methods based on dictionary learning for inverse scattering problems,” in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, pp. 2123-2126, 2022.

R. Scapaticci, P. Kosmas, and L. Crocco, “Wavelet-based regularization for robust microwave imaging in medical applications,” IEEE Transactions on Biomedical Engineering, vol. 62, no. 4, pp. 1195-1202, 2014.

O. Mudanyali, S. Yildiz, O. Semerci, A. Yapar, and I. Akduman, “A microwave tomographic approach for nondestructive testing of dielectric coated metallic surfaces,” IEEE Geoscience and Remote Sensing Letters, vol. 5, no. 2, pp. 180-184, 2008.

C. Yang, J. Zhang, and M. S. Tong, “An FFT-accelerated particle swarm optimization method for solving far-field inverse scattering problems,” IEEE Transactions on Antennas and Propagation, vol. 69, no. 2, pp. 1078-1093, 2020.

M. S. Tong, K. Yang, W. T. Sheng, and Z. Y. Zhu, “Efficient reconstruction of dielectric objects based on integral equation approach with Gauss-Newton minimization,” IEEE Transactions on Image Processing, vol. 22, no. 12, pp. 4930-4937, Dec. 2013.

M. S. Tong and C. X. Yang, “A meshless method of solving inverse scattering problems for imaging dielectric objects,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 2, pp. 990-999, Feb. 2016.

L. E. Sun and M. S. Tong, “Reconstruction of 3D anisotropic objects by VIE and model-based inversion methods,” Progress in Electromagnetics Research C, vol. 83, pp. 97-111, 2018.

C. X. Yang, J. Zhang, and M. S. Tong, “A hybrid inversion method based on the bat algorithm for microwave imaging of two-dimensional dielectric scatterers,” Progress in Electromagnetics Research M, vol. 102, pp. 91-104, Jan. 2021.

P. M. Van den Berg and A. Abubakar, “Contrast source inversion method: State of art,” Progress in Electromagnetics research, vol. 34, no. 11, pp. 189-218, 2001.

W. C. Chew and Y.-M. Wang, “Reconstruction of two-dimensional permittivity distribution using the distorted Born iterative method,” IEEE Transactions on Medical Imaging, vol. 9, no. 2, pp. 218-225, 1990.

C. X. Yang and M. S. Tong, “A novel diffraction tomographic algorithm for remote reconstruction with limited observation angles,” International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, vol. 32, no. 5, pp. 1-10, 2019.

Y. He, L. Zhang, and Mei Song Tong, “Microwave imaging of 3D dielectric-magnetic penetrable objects based on integral equation method,” IEEE Transactions on Antennas and Propagation, vol. 71, no. 6, pp. 5110-5120, June 2023.

B. Q. Wang, D. T. Zhang, and M. S. Tong, “Microwave imaging for highly-anisotropic objects based on Gauss-Newton minimization method,” in 2023 Progress in Electromagnetics Research Symposium, Prague, Czech, July 2023.

C. X. Yang, J. Zhang, and M. S. Tong, “A hybrid quantum-behaved particle swarm optimization algorithm for solving inverse scattering problems,” IEEE Transactions on Antennas and Propagation, vol. 69, no. 9, pp. 5861-5869, 2021.

M. S. Tong and W. C. Chew, “Multilevel fast multipole acceleration in the Nyström discretization of surface electromagnetic integral equations for electromagnetic scattering by composite objects,” IEEE Transactions on Antennas and Propagation, vol. 58, no. 10, pp. 3411-3416, Oct.2010.

A. Onufriev, D. A. Case, and D. Bashford, “Effective Born radii in the generalized Born approximation: The importance of being perfect,” Journal of Computational Chemistry, vol. 23, no. 14, pp. 1297-1304, 2002.

A. Devaney, “Inverse-scattering theory within the Rytov approximation,” Optics Letters, vol. 6, no. 8, pp. 374-376, 1981.

X. Chen, Computational Methods for Electromagnetic Inverse Scattering. Hoboken, NJ, USA: Wiley, 2018.

Z. Wei and X. Chen, “Solving full-wave nonlinear inverse scattering problems by deep learning schemes,” in 2019 IEEE International Conference on Computational Electromagnetics (ICCEM), pp. 1-2, IEEE, 2019.

X. Chen and Z. Wei, “Generalization capabilities of deep learning schemes in solving inverse scattering problems,” in 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, pp. 215-216, IEEE, 2019.

Y. Zhou, Y. Zhong, Z. Wei, T. Yin, and X. Chen, “An improved deep learning scheme for solving 2-D and 3-D inverse scattering problems,” IEEE Transactions on Antennas and Propagation, vol. 69, no. 5, pp. 2853-2863, 2020.

L. Ahmadi and A. A. Shishegar, “Embedding a priori information in inverse scattering problems using deep learning,” in 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI), pp. 2006-2007, IEEE, 2021.

H. Yang and J. Liu, “A qualitative deep learning method for inverse scattering problems,” Applied Computational Electromagnetics Society (ACES) Journal, pp. 153-160, 2020.

C. Liu, L. Li, and T. Cui, “Physics-informed unsupervised deep learning framework for solving full-wave inverse scattering problems,” in 2022 IEEE Conference on Antenna Measurements and Applications (CAMA), pp. 1-4, IEEE,2022.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, part III 18, pp. 234-241, Springer, 2015.

Z. Ahmed, S. A. Tanim, F. S. Prity, H. Rahman, and T. B. M. Maisha, “Improving biomedical image segmentation: An extensive analysis of U-Net for enhanced performance,” in 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE), pp. 1-6, IEEE, 2024.

M. I. Hosen and M. B. Islam, “Masked face inpainting through residual attention UNet,” in 2022 Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1-5, IEEE,2022.

M.-H. Guo, T.-X. Xu, J.-J. Liu, Z.-N. Liu, P.-T. Jiang, T.-J. Mu, S.-H. Zhang, R. R. Martin, M.-M. Cheng, and S.-M. Hu, “Attention mechanisms in computer vision: A survey,” Computational Visual Media, vol. 8, no. 3, pp. 331-368, 2022.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.

K. Belkebir and M. Saillard, “Testing inversion algorithms against experimental data,” Inverse Problems, vol. 17, no. 6, p. 1565, 2001.

Downloads

Published

2024-11-30

How to Cite

[1]
C. X. . Yang, J. J. . Meng, S. . Wei, and M. S. . Tong, “A Dual-input Electromagnetic Inverse Scattering Algorithm Based on Improved U-net”, ACES Journal, vol. 39, no. 11, pp. 961–969, Nov. 2024.

Issue

2024: Vol 39 Iss 11

Section

General Submission

Categories