Wavelet-based  ${{L}_{{1}/{2}}}$  regularization for CS-TomoSAR imaging of forested area

doi:10.23919/JSEE.2020.000088

Journal of Systems Engineering and Electronics ›› 2020, Vol. 31 ›› Issue (6): 1160-1166.doi: 10.23919/JSEE.2020.000088

• DEFENCE ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

Wavelet-based ${{L}_{{1}/{2}}}$ regularization for CS-TomoSAR imaging of forested area

Hui BI^1,*(), Yuan CHENG¹(), Daiyin ZHU¹(), Wen HONG²()

¹ College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
² Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

Received:2020-05-06 Online:2020-12-18 Published:2020-12-29
Contact: Hui BI E-mail:bihui@nuaa.edu.cn;nuaachengyuan@nuaa.edu.cn;zhudy@nuaa.edu.cn;whong@mail.ie.ac.cn
About author:|BI Hui was born in 1991. He received his Ph.D. degree in signal and information processing from University of Chinese Academy of Sciences, Beijing, China, in 2017. From 2012 to 2017, he worked in the Science and Technology on Microwave Imaging Laboratory, Institute of Electronics, Chinese Academy of Sciences, China. He was a research fellow with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, from 2017 to 2018. Since 2018, he has been working in the College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China, as an associate professor. His main research interests are sparse microwave imaging with compressive sensing, synthetic aperture radar data processing and application, sparse signal processing, and tomographic SAR imaging. E-mail: bihui@nuaa.edu.cn||CHENG Yuan was born in 1991. He received his B.S. degree in electronic engineering from Nanjing University of Information Science and Technology, and his M.S. degree in electronics from China Ship Research and Development Academy. He is currently pursuing his Ph.D. degree with Nanjing University of Aeronautics and Astronautics, Nanjing, China. His research interests include radar signal processing and its applications. E-mail: nuaachengyuan@nuaa.edu.cn||ZHU Daiyin was born in 1974. He received his B.S. degree in electronic engineering from the Southeast University, Nanjing, China, in 1996, and his M.S. and Ph.D. degrees in electronics from the Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing, China, in 1998 and 2002, respectively. From 1998 to 1999, he was a guest scientist with the Institute of Radio Frequency Technology, German Aerospace Center, Oberpfaffenhofen. In 1998, he joined the Department of Electronic Engineering, NUAA, where he is currently a professor. His current research interests include radar imaging algorithms, SAR ground moving target indication, SAR/ISAR autofocus techniques, and SAR interferometry. E-mail: zhudy@nuaa.edu.cn||HONG Wen was born in 1968. She received her Ph.D. degree from Beihang University, Beijing, China, in 1997. She was with the Department of Electrical Engineering, Beihang University, as a faculty member in signal and information processing from 1997 to 2002. In between, she worked with the Institute of Radio Frequency Technology, German Aerospace Center, Wessling, Germany, as a guest scientist from 1998 to 1999. Since 2002, she has been working in the Institute of Electronics, Chinese Academy of Sciences, where she is currently a professor. Her main research interests are polarimetric/polarimetric interferometric synthetic aperture radar data processing and application, 3-D SAR signal processing, circular SAR signal processing and sparse microwave imaging with compressed sensing. E-mail: whong@mail.ie.ac.cn
Supported by:
This work was supported by the Fundamental Research Funds for the Central Universities (NE2020004), the National Natural Science Foundation of China (61901213), the Natural Science Foundation of Jiangsu Province (BK20190397), the Aeronautical Science Foundation of China (201920052001), the Young Science and Technology Talent Support Project of Jiangsu Science and Technology Association, and the Foundation of Graduate Innovation Center in Nanjing University of Aeronautics and Astronautics (kfjj20200419).

Abstract

Abstract:

Tomographic synthetic aperture radar (TomoSAR) imaging exploits the antenna array measurements taken at different elevation aperture to recover the reflectivity function along the elevation direction. In these years, for the sparse elevation distribution, compressive sensing (CS) is a developed favorable technique for the high-resolution elevation reconstruction in TomoSAR by solving an $L_1 $ regularization problem. However, because the elevation distribution in the forested area is non-sparse, if we want to use CS in the recovery, some basis, such as wavelet, should be exploited in the sparse representation of the elevation reflectivity function. This paper presents a novel wavelet-based $L_{{1}/{2}} $ regularization CS-TomoSAR imaging method of the forested area. In the proposed method, we first construct a wavelet basis, which can sparsely represent the elevation reflectivity function of the forested area, and then reconstruct the elevation distribution by using the $L_{{1}/{2}} $ regularization technique. Compared to the wavelet-based $L_1 $ regularization TomoSAR imaging, the proposed method can improve the elevation recovered quality efficiently.

Key words: tomographic synthetic aperture radar (TomoSAR), compressive sensing (CS), $L_{{1}/{2}} $ regularization, wavelet basis

Hui BI, Yuan CHENG, Daiyin ZHU, Wen HONG. Wavelet-based ${{L}_{{1}/{2}}}$ regularization for CS-TomoSAR imaging of forested area[J]. Journal of Systems Engineering and Electronics, 2020, 31(6): 1160-1166.

Figures/Tables 8

Fig 1

Fig 2

Fig 3

Fig 4

Fig 5

Fig 6

References 18

1	REIGBER A, MOREIRA A First demonstration of airborne SAR tomography using multibaseline L-band data. IEEE Trans. on Geoscience and Remote Sensing, 2000, 38 (5): 2142- 2152. doi: 10.1109/36.868873
2	FORNARO G, SERAFINO F, LOMBARDINI F Three-dimensional multipass SAR focusing: experiments with long-term space-borne data. IEEE Trans. on Geoscience and Remote Sensing, 2005, 43 (4): 702- 714. doi: 10.1109/TGRS.2005.843567
3	ZHU X X, BAMLER R Tomographic SAR inversion by L₁-norm regularization— the compressive sensing approach . IEEE Trans. on Geoscience and Remote Sensing, 2010, 48 (10): 3839- 3846. doi: 10.1109/TGRS.2010.2048117
4	DONOHO D Compressed sensing. IEEE Trans. on Information Theory, 2006, 52 (4): 1289- 1306. doi: 10.1109/TIT.2006.871582
5	CANDES E, TAO T Near-optimal signal recovery from random projections: universal encoding strategies. IEEE Trans. on Information Theory, 2006, 52 (12): 5406- 5425. doi: 10.1109/TIT.2006.885507
6	NYQUIST H Certain topics in telegraph transmission theory. Transactions of the American Institute of Electrical Engineers, 1928, 47 (2): 617- 644. doi: 10.1109/T-AIEE.1928.5055024
7	SHANNON C E Communication in the presence of noise. Proceedings of the Institute of Radio Engineers, 1949, 37 (1): 10- 21.
8	CANDES E, TAO T Decoding by linear programming. IEEE Trans. on Information Theory, 2005, 51 (12): 4203- 4215. doi: 10.1109/TIT.2005.858979
9	CANDES E, ROMBERG J, TAO T Stable signal recovery from incomplete and inaccurate measurement. Communications on Pure and Applied Mathematics, 2006, 59 (8): 1207- 1223. doi: 10.1002/cpa.20124
10	XU Z, CHANG X, XU F, et al L_1/2 regularization: a thresholding representation theory and a fast solver . IEEE Trans. on Neural Networks and Learning Systems, 2012, 23 (7): 1013- 1027. doi: 10.1109/TNNLS.2012.2197412
11	ZHU X X, BAMLER R. Very high resolution SAR tomography via compressive sensing. Proc. of the Fringe Workshop, 2009: 1−7.
12	BUDILLON A, EVANGELISTA A, SCHIRINZI G. SAR tomography from sparse samples. Proc. of the International Geoscience and Remote Sensing Symposium, 2009: 865−868.
13	MANCON S, TEBALDINI S, GUARNIERI A M. Lp norm SAR tomography by iteratively reweighted least square: first results. Proc. of the IEEE Geoscience and Remote Sensing Symposium, 2014: 1309−1312.
14	WANG X, XU F, JIN Y Q. Numerical simulation of tomography-SAR imaging and the object reconstruction using the compressive sensing approach with $L_ {{1}/{2}}$-norm regularization. Proc. of the International Scientific Radio Union General Assembly and Scientific Symposium, 2014: 1−4.
15	BUDILLON A, FERRAIOLI G, SCHIRINZI G Localization performance of multiple scatterers in compressive sampling SAR tomography: results on COSMO-SkyMed data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7 (7): 2902- 2910. doi: 10.1109/JSTARS.2014.2344916
16	AGUILERA E, NANNINI M, REIGBER A Wavelet-based compressed sensing for SAR tomography of forested areas. IEEE Trans. on Geoscience and Remote Sensing, 2013, 51 (12): 5283- 5295. doi: 10.1109/TGRS.2012.2231081
17	NANNINI M, SCHEIBER R, HORN R, et al First 3-D reconstructions of targets hidden beneath foliage by means of polarimetric SAR tomography. IEEE Geoscience and Remote Sensing Letters, 2012, 9 (1): 60- 64. doi: 10.1109/LGRS.2011.2160329
18	STRANG G, NGUYEN T. Wavelet and filter banks. Wellesley, MA: Wellesley College, 1997.

[1]	Wei SONG, Wenzheng WANG. Compressive sensing based multiuser detector for massive MBM MIMO uplink [J]. Journal of Systems Engineering and Electronics, 2020, 31(1): 19-27.
[2]	Shixin WANG, Yuan ZHAO, Ibrahim LAILA, Ying XIONG, Jun WANG, Bin TANG. Joint 2D DOA and Doppler frequency estimation for L-shaped array using compressive sensing [J]. Journal of Systems Engineering and Electronics, 2020, 31(1): 28-36.
[3]	Chaozhu ZHANG, Hongyi XU, Haiqing JIANG. Adaptive block greedy algorithms for receiving multi-narrowband signal in compressive sensing radar reconnaissance receiver [J]. Journal of Systems Engineering and Electronics, 2018, 29(6): 1158-1169.
[4]	Hongyi Xu, Haiqing Jiang, and Chaozhu Zhang. Multi-narrowband signals receiving method based on analog-to-information convertor and block sparsity [J]. Systems Engineering and Electronics, 2017, 28(4): 643-.
[5]	Youhua Wang and Jianqiu Zhang. Robust signal recovery algorithm for structured perturbation compressive sensing [J]. Journal of Systems Engineering and Electronics, 2016, 27(2): 319-325.
[6]	Yi Shen, Yan Jing, and Naizhang Feng. Construction of deterministic sensing matrix and its application to DOA estimation [J]. Systems Engineering and Electronics, 2016, 27(1): 10-.
[7]	Jia Li, Qiang Wang, and Yi Shen. Near optimal condition of OMP algorithm in recovering sparse signal from noisy measurement [J]. Journal of Systems Engineering and Electronics, 2014, 25(4): 547-.
[8]	Shuyi Jia, Guohong Wang, and Shuncheng Tan. Radial acceleration estimation of multiple high maneuvering targets [J]. Journal of Systems Engineering and Electronics, 2014, 25(2): 183-193.
[9]	Wei Gan, Luping Xu, and Hua Zhang. Maximal-minimal correlation atoms algorithm for sparse recovery [J]. Journal of Systems Engineering and Electronics, 2013, 24(4): 579-585.
[10]	Chunhui Zhao and Wei Liu. Degradation algorithm of compressive sensing [J]. Journal of Systems Engineering and Electronics, 2011, 22(5): 832-839.

Wavelet-based ${{L}_{{1}/{2}}}$ regularization for CS-TomoSAR imaging of forested area

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

Share this article

Figures/Tables 8

References 18

Related Articles 10

Recommended Articles

Metrics

Comments