Extraction of Attributed Scattering Center Based on Physics Informed Machine Learning

YUE Ziyu; XU Feng

doi:10.11999/JEIT231215

Volume 46 Issue 5

May 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(5): 2036-2047

YUE Ziyu, XU Feng. Extraction of Attributed Scattering Center Based on Physics Informed Machine Learning[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2036-2047. doi: 10.11999/JEIT231215

Citation:

YUE Ziyu, XU Feng. Extraction of Attributed Scattering Center Based on Physics Informed Machine Learning[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2036-2047. doi: 10.11999/JEIT231215

Citation:

PDF( 10250 KB)

Extraction of Attributed Scattering Center Based on Physics Informed Machine Learning

doi: 10.11999/JEIT231215

YUE Ziyu,
XU Feng^,

Key Laboratory for Information Science of Electromagnetic Waves(MoE), Fudan University, Shanghai 200433, China

Funds: The National Natural Science Foundation of China (61991422)

Received Date: 2023-11-02
Rev Recd Date: 2024-04-22

Available Online: 2024-05-06

Publish Date: 2024-05-10

Abstract

Abstract

To estimate parameters of parameterized scattering center models is one of the basic methods for Synthetic Aperture Radar Advanced Information Retrieval (SAR AIR). Traditional Attributed Scattering Center (ASC) parameter estimation algorithms usually suffer from issues such as slow computation speed, high algorithm complexity, and high sensitivity to initial values of parameters. In this paper, a novel end-to-end framework for inverting ASC parameters from radar images based on unsupervised deep learning is proposed. Firstly, an autoencoder network structure is employed to effectively extract image features of targets, alleviating the difficulties solving directly caused by the complex non-convex optimization space and resolving the sensitivity to initial values. Secondly, the ASC model is embedded as a physical decoder to constrain the encoder output to correct ASC parameters. Finally, the end-to-end architecture are utlized to train and infer the model, achieving the purpose of reducing algorithm complexity and improving estimation speed. Through testing on simulated and measured data, experimental results indicate that the estimation error obtained on the SAR image test set with a resolution of 0.15 m is less than 0.1 m while the average processing time is 0.06 s for the inversion of one single scattering center, which demonstrate the effectiveness, efficiency, and robustness of the proposed approach.
- Attributed Scattering Center(ASC) model,
- Parameter estimation,
- Physics informed machine learning

FullText(HTML)

References(18)

References

[1]	徐丰, 王海鹏, 金亚秋. 合成孔径雷达图像智能解译[M]. 北京: 科学出版社, 2020: 2–5. XU Feng, WANG Haipeng, and JIN Yaqiu. Intelligent Interpretation of Synthetic Aperture Radar Images[M]. Beijing: Science Press, 2020: 2–5.
[2]	CHEN Jiankun, PENG Lingxiao, QIU Xiaolan, et al. A 3D building reconstruction method for SAR images based on deep neural network[J]. Scientia Sinica Informationis, 2019, 49(12): 1606–1625. doi: 10.1360/SSI-2019-0100.
[3]	PENG Lingxiao, QIU Xiaolan, DING Chibiao, et al. Generating 3d point clouds from a single SAR image using 3D reconstruction network[C]. IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019: 3685–3688. doi: 10.1109/IGARSS.2019.8900449.
[4]	邢孟道, 谢意远, 高悦欣, 等. 电磁散射特征提取与成像识别算法综述[J]. 雷达学报, 2022, 11(6): 921–942. doi: 10.12000/JR22232. XING Mengdao, XIE Yiyuan, GAO Yuexin, et al. Electromagnetic scattering characteristic extraction and imaging recognition algorithm: A review[J]. Journal of Radars, 2022, 11(6): 921–942. doi: 10.12000/JR22232.
[5]	计科峰. SAR图像目标特征提取与分类方法研究[D]. [博士论文], 中国人民解放军国防科技大学, 2003. JI Kefeng. Research on SAR image target feature extraction and classification methods[D]. [Ph. D. dissertation], National University of Defense Technology, 2003.
[6]	段佳, 张磊, 盛佳恋, 等. 独立属性散射中心参数降耦合估计方法[J]. 电子与信息学报, 2012, 34(8): 1853–1859. doi: 10.3724/SP.J.1146.2011.01302. DUAN Jia, ZHANG Lei, SHENG Jialian, et al. Parameters decouple and estimation of independent attributed scattering centers[J]. Journal of Electronics & Information Technology, 2012, 34(8): 1853–1859. doi: 10.3724/SP.J.1146.2011.01302.
[7]	石志广, 周剑雄, 赵宏钟, 等. 基于协同粒子群优化的GTD模型参数估计方法[J]. 电子学报, 2007, 35(6): 1102–1107. doi: 10.3321/j.issn:0372-2112.2007.06.020. SHI Zhiguang, ZHOU Jianxiong, ZHAO Hongzhong, et al. A GTD scattering center model parameter estimation method based on CPSO[J]. Acta Electronica Sinica, 2007, 35(6): 1102–1107. doi: 10.3321/j.issn:0372-2112.2007.06.020.
[8]	李飞, 纠博, 刘宏伟, 等. 基于稀疏表示的SAR图像属性散射中心参数估计算法[J]. 电子与信息学报, 2014, 36(4): 931–937. doi: 10.3724/SP.J.1146.2013.00576. LI Fei, JIU Bo, LIU Hongwei, et al. Sparse representation based algorithm for estimation of attributed scattering center parameter on SAR imagery[J]. Journal of Electronics & Information Technology, 2014, 36(4): 931–937. doi: 10.3724/SP.J.1146.2013.00576.
[9]	YANG Dongwen, NI Wei, DU Lan, et al. Efficient attributed scatter center extraction based on image-domain sparse representation[J]. IEEE Transactions on Signal Processing, 2020, 68: 4368–4381. doi: 10.1109/tsp.2020.3011332.
[10]	XIE Yiyuan, XING Mengdao, GAO Yuexin, et al. Attributed scattering center extraction method for microwave photonic signals using DSM-PMM-regularized optimization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5230016. doi: 10.1109/TGRS.2022.3183855.
[11]	ZHANG Jinsong, XING Mengdao, and XIE Yiyuan. FEC: A feature fusion framework for SAR target recognition based on electromagnetic scattering features and deep CNN features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(3): 2174–2187. doi: 10.1109/TGRS.2020.3003264.
[12]	FENG Sijia, JI Kefeng, WANG Fulai, et al. Electromagnetic scattering feature (ESF) module embedded network based on ASC model for robust and interpretable SAR ATR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5235415. doi: 10.1109/TGRS.2022.3208333.
[13]	FENG Sijia, JI Kefeng, ZHANG Linbin, et al. SAR target classification based on integration of ASC parts model and deep learning algorithm[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 10213–10225. doi: 10.1109/JSTARS.2021.3116979.
[14]	GERRY M J, POTTER L C, GUPTA I J, et al. A parametric model for synthetic aperture radar measurements[J]. IEEE Transactions on Antennas and Propagation, 1999, 47(7): 1179–1188. doi: 10.1109/8.785750.
[15]	POTTER L C and MOSES R L. Attributed scattering centers for SAR ATR[J]. IEEE Transactions on Image Processing, 1997, 6(1): 79–91. doi: 10.1109/83.552098.
[16]	KINGMA D P and WELLING M. Auto-encoding variational bayes[J]. arXiv: 1312.6114, 2013. doi: 10.48550/arXiv.1312.6114.
[17]	HORIE M, MORITA N, HISHINUMA T, et al. Isometric transformation invariant and equivariant graph convolutional networks[C]. The ICLR 2021, https://doi.org/10.48550/arXiv.2005.06316.
[18]	MENG Chuizheng, SEO S, CAO Defu, et al. When physics meets machine learning: A survey of physics-informed machine learning[J]. arXiv: 2203.16797, 2022. doi: 10.48550/arXiv.2203.16797.