volume | PIER Journals

Abstract

Millimeter wave synthetic aperture interferometric radiometer (SAIR) can achieve high-resolution imaging without a large physical aperture antenna and has strong application advantages in the fields of earth remote sensing, astronomical observation, and meteorological monitoring. In order to adapt to various payload platforms and detection needs, the existing SAIR array structures are diverse, but the existing imaging methods are difficult to effectively deal with various arrays and achieve stable high-precision imaging inversion. Thus, this paper proposes a general multi-channel fusion imaging network to achieve SAIR imaging inversion of any array structure. First, with the help of the sensor matrix deduction subnet, a high-precision imaging sensor matrix is deduced according to the position of each array element of the SAIR system, and then high-precision image reconstruction is achieved with the help of the multi-channel fusion imaging subnet. The simulation results show that the network has good adaptability and can achieve high-precision imaging inversion of different SAIR array structures.

1. Martàn-Neira, M., D. M. LeVine, Y. Kerr, N. Skou, M. Peichl, A. Camps, I. Corbella, M. Hallikainen, J. Font, J. Wu, S. Mecklenburg, and M. Drusch, "Microwave interferometric radiometry in remote sensing: An invited historical review," Radio Science, Vol. 49, No. 6, 415-449, 2014.

2. LeVine, D. M., "Synthetic aperture radiometer systems," IEEE Transactions on Microwave Theory and Techniques, Vol. 47, No. 12, 2228-2236, Dec. 1999.
doi:10.1109/22.808964

3. Zhou, Xi, Houjun Sun, Jiwei He, and Xin Lu, "NUFFT-based iterative reconstruction algorithm for synthetic aperture imaging radiometers," IEEE Geoscience and Remote Sensing Letters, Vol. 6, No. 2, 273-276, 2009.

4. Chen, Liangbing, Wenfeng Ma, Yuhao Wang, and Huilin Zhou, "Array factor forming with regularization for aperture synthesis radiometric imaging with an irregularly distributed array," IEEE Geoscience and Remote Sensing Letters, Vol. 17, No. 1, 97-101, 2020.

5. Zhang, Yun, Xin Qi, Yicheng Jiang, Wanling Liao, and Yuhan Du, "Reconstruction algorithm for staggered synthetic aperture radar with modified second-order keystone transform," Journal of Applied Remote Sensing, Vol. 15, No. 2, 026511, 2021.

6. Li, Hao, Chengwang Xiao, Jian Dong, Wenjing Wang, Yinan Li, Liangbing Chen, and Haofeng Dou, "Image reconstruction method of 1-D nonuniform aperture synthesis radiometers based on convolutional neural network," 2024 International Conference on Microwave and Millimeter Wave Technology (ICMMT), Vol. 1, 1-3, Bejing, China, May 2024.

7. Feng, Li, Qingxia Li, Ke Chen, Yufang Li, Xiaolin Tong, Xinqiang Wang, Hailiang Lu, and Yan Li, "The gridding method for image reconstruction of nonuniform aperture synthesis radiometers," IEEE Geoscience and Remote Sensing Letters, Vol. 12, No. 2, 274-278, 2015.

8. Andersson, Fredrik, Randolph Moses, and Frank Natterer, "Fast Fourier methods for synthetic aperture radar imaging," IEEE Transactions on Aerospace and Electronic Systems, Vol. 48, No. 1, 215-229, 2012.

9. Wu, Yuanchao, Yinan Li, Guangnan Song, Haofeng Dou, Dandan Wen, Pengfei Li, Xiaojiao Yang, Rongchuan Lv, and Hao Li, "A near-field imaging method based on the near-field distance for an aperture synthesis radiometer," Remote Sensing, Vol. 16, No. 5, 767, 2024.

10. Zhang, Yilong, Yuan Ren, Wei Miao, Zhenhui Lin, Hao Gao, and Shengcai Shi, "Microwave SAIR imaging approach based on deep convolutional neural network," IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, No. 12, 10376-10389, 2019.

11. Xiao, Chengwang, Xi Wang, Haofeng Dou, Hao Li, Rongchuan Lv, Yuanchao Wu, Guangnan Song, Wenjin Wang, and Ren Zhai, "Non-uniform synthetic aperture radiometer image reconstruction based on deep convolutional neural network," Remote Sensing, Vol. 14, No. 10, 2359, 2022.

12. Faucheron, Richard, Eric Anterrieu, Louise Yu, Ali Khazaal, and Nemesio J. Rodríguez-Fernández, "Deep learning based approach in imaging radiometry by aperture synthesis: An alias-free method," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 17, 6693-6711, 2024.

13. Dou, Haofeng, Chengwang Xiao, Hao Li, Yinan Li, Pengju Dang, Rongchuan Lv, Pengfei Li, Guangnan Song, Yuanchao Wu, Xiaojiao Yang, et al., "Deep learning imaging for 1-D aperture synthesis radiometers," IEEE Transactions on Geoscience and Remote Sensing, Vol. 61, 1-16, 2023.

14. Xiao, Chengwang, Haofeng Dou, Hao Li, Rong Jin, Ren Zhai, Wenjing Wang, Rongchuan Lv, and Yinan Li, "Image reconstruction of synthetic aperture radiometer by transformer," IEEE Transactions on Geoscience and Remote Sensing, Vol. 61, 1-15, 2023.

15. Corbella, I., N. Duffo, M. Vall-llossera, A. Camps, and F. Torres, "The visibility function in interferometric aperture synthesis radiometry," IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, No. 8, 1677-1682, 2004.
doi:10.1109/TGRS.2004.830641

16. Chen, Jianfei, Yuehua Li, Jianqiao Wang, Yuanjiang Li, and Yilong Zhang, "Regularization imaging algorithm with accurate G matrix for near-field MMW synthetic aperture imaging radiometer," Progress In Electromagnetics Research B, Vol. 58, 193-203, 2014.

17. Wu, Yuanchao, Yinan Li, Guangnan Song, Haofeng Dou, Dandan Wen, Pengfei Li, Xiaojiao Yang, Rongchuan Lv, and Hao Li, "A near-field imaging method based on the near-field distance for an aperture synthesis radiometer," Remote Sensing, Vol. 16, No. 5, 767, 2024.

18. Feng, Xinxin, Xianguo Li, and Jianxiong Li, "Multi-scale fractal residual network for image super-resolution," Applied Intelligence, Vol. 51, No. 4, 1845-1856, 2021.

19. Ma, Yimin, Yi Xu, Yunqing Liu, Fei Yan, Qiong Zhang, Qi Li, and Quanyang Liu, "Multi-scale cross-attention fusion network based on image super-resolution," Applied Sciences, Vol. 14, No. 6, 2634, 2024.

20. Xiang, Jinxi, Yonggui Dong, and Yunjie Yang, "FISTA-Net: Learning a fast iterative shrinkage thresholding network for inverse problems in imaging," IEEE Transactions on Medical Imaging, Vol. 40, No. 5, 1329-1339, 2021.

21. Tang, Hailiang, Wenxiao Zhang, Hailin Zhu, and Ke Zhao, "Self-supervised real-world image denoising based on multi-scale feature enhancement and attention fusion," IEEE Access, Vol. 12, 49720-49734, 2024.

22. Cao, Ran, Leyuan Fang, Ting Lu, and Nanjun He, "Self-attention-based deep feature fusion for remote sensing scene classification," IEEE Geoscience and Remote Sensing Letters, Vol. 18, No. 1, 43-47, 2021.

23. Chen, Yuantao, Runlong Xia, Kai Yang, and Ke Zou, "MFFN: Image super-resolution via multi-level features fusion network," The Visual Computer, Vol. 40, No. 2, 489-504, 2024.

24. Zhang, Yulun, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu, "Residual dense network for image super-resolution," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vol. 43, No. 7, 2480-2495, Salt Lake City, UT, USA, Jun. 2018.

25. Liu, Yichao, Zongru Shao, and Nico Hoffmann, "Global attention mechanism: Retain information to enhance channel-spatial interactions," arXiv preprint arXiv:2112.05561, 2021.

Dr. Chenggong Zhang

Dr. Jianfei Chen

Dr. Jiahao Yu

Dr. Yujie Ruan

Dr. Sheng Zhang

Dr. Shujin Zhu

Dr. Leilei Liu