Fast SAR autofocus based on convolutional neural networks

doi:10.11947/j.AGCS.2024.20230281

Abstract

Abstract:

Autofocus is a key technology for high-resolution synthetic aperture radar imaging. However, traditional SAR autofocus methods require too many iterations, have low computational efficiency, and are unsuitable for on-orbit processing. This paper proposes a fast SAR autofocus method based on convolutional neural networks. This method utilizes CNNs to learn the mapping from defocused images to focused images, mainly designed to correct the azimuth phase errors. It has a real-time performance and is more suitable for on-orbit processing since it does not need to iterate or adjust parameters in the testing phase. Experimental results on real SAR data show that our proposed method has the highest focusing quality and speed.

Key words: convolutional neural networks, SAR, phase error, autofocus

CLC Number:

P237

Zhi LIU, Shuyuan YANG, Zifan YU, Zhixi FENG, Quanwei GAO, Min WANG. Fast SAR autofocus based on convolutional neural networks[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(4): 610-619.

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References 31

[1]	李涛, 唐新明, 高小明, 等. SAR卫星业务化地形测绘能力分析与展望[J]. 测绘学报, 2021, 50(7):891-904. DOI: 10.11947/j.AGCS.2021.20200199.
	LI Tao, TANG Xinming, GAO Xiaoming, et al. Analysis and outlook of the operational topographic surveying and mapping capability of the SAR satellites[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(7):891-904. DOI: 10.11947/j.AGCS.2021.20200199.
[2]	刘银中. 机载SAR 实测数据多普勒参数估计与成像算法研究[D]. 哈尔滨: 哈尔滨工业大学, 2006.
	LIU Yinzhong. Research on Doppler parameter estimation and imaging algorithm of airborne SAR measured data [D]. Harbin: Harbin Industrial University, 2006.
[3]	王越. 合成孔径雷达自聚焦算法研究[D]. 南京: 南京航空航天大学, 2006.
	WANG Yue. Research on autofocus algorithm of synthetic aperture radar[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2006.
[4]	KIM J W, KIM Y D, YEO T D, et.al. Fast Fourier-domain optimization using hybrid L₁-L_p-Norm for autofocus in airborne SAR imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(10):7934-7954.
[5]	IAN G C, FRANK H C W. Digital processing of synthetic aperture radar data: algorithms and implementation[M]. Norwood: Artech House, 2005: 1-436.
[6]	SAMCZYNSKI P, KULPA K S. Coherent mapdrift technique[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 48(3):1505-1517.
[7]	CALLOWAY T, DONOHOE G. Subaperture autofocus for synthetic aperture radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 1994, 30(2):617-621.
[8]	YANG Ruliang, LI Haiying, LI Shiqiang, et al. High-resolution microwave imaging[M]. Singapore: Springer, 2018: 1-569.
[9]	LIU Zhi, YANG Shuyuan, FENG Zhixi, et al. Fast SAR autofocus based on ensemble convolutional extreme learning machine[J]. Remote Sensing, 2021, 13(14):2683.
[10]	LI Xi, LIU Guosui, NI Jinlin. Autofocusing of ISAR images based on entropy minimization[J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(4):1240-1252.
[11]	CAI Jinjian, MARTORELLA M, CHANG Shaoqiang, et al. Efficient nonparametric ISAR autofocus algorithm based on contrast maximization and Newton's method[J]. IEEE Sensors Journal, 2020, 21(4):4474-4487.
[12]	MORRISON R L, DO M N, MUNSON D C. SAR image autofocus by sharpness optimization: a theoretical study[J]. IEEE Transactions on Image Processing, 2007, 16(9):2309-2321.
[13]	ZHANG Shuanghui, LIU Yongxiang, LI Xiang. Fast entropy minimization based autofocusing technique for ISAR imaging[J]. IEEE Transactions on Signal Processing, 2015, 63(13):3425-3434.
[14]	ZENG Tao, WANG Rui, LI Feng. SAR image autofocus utilizing minimum-entropy criterion[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(6):1552-1556.
[15]	WAHL D E, EICHEL P H, GHIGLIA D C, et al. Phase gradient autofocus: a robust tool for high resolution SAR phase correction[J]. IEEE Transactions on Aerospace and electronic systems, 1994, 30(3):827-835.
[16]	卿吉明, 徐浩煜, 梁兴东, 等. 一种可用于实时成像的改进PGA算法[J]. 雷达学报, 2015, 4(5):600-607.
	QING Jiming, XU Haoyu, LIANG Xingdong, et al. An improved phase gradient autofocus algorithm used in real-time processing[J]. Journal of Radars, 2015, 4(5):600-607.
[17]	LEE H, JUNG C S, KIM K W. Feature preserving autofocus algorithm for phase error correction of SAR images[J]. Sensors, 2021, 21(7):2370.
[18]	郭华东, 吴文瑾, 张珂, 等. 新型SAR对地环境观测[J]. 测绘学报, 2022, 51(6):862-872. DOI: 10.11947/j.AGCS.2022.20220098.
	GUO Huadong, WU Wenjin, ZHANG Ke, et al. New generation SAR for Earth environment observation[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(6):862-872. DOI: 10.11947/j.AGCS.2022.20220098.
[19]	QIAO Guangkai, DAI Jingwei, WANG Kaizhi, et al. A finely focusing method of SAR using very deep neural network[C]//Proceedings of the 39th IEEE International Geoscience and Remote Sensing Symposium. Yokohama: IEEE, 2019: 2571-2574.
[20]	LIU Zhi, YANG Shuyuan, GAO Quanwei, et al. AFnet and PAFnet: fast and accurate SAR autofocus based on deep learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022(60):1-13.
[21]	HUO Jiawei, LI Min, LI Zhongyu, et al. Novel SAR autofocus method based on deep learning with GAN network[C]//Proceedings of 2021 CIE International Conference on Radar. Haikou: IEEE, 2021: 800-803.
[22]	WEI Pu. Deep learning algorithm for SAR autofocus[C]//Proceedings of 2021 General assembly and scientific symposium of the international union of radio science. Rome: IEEE, 2021: 1-4.
[23]	AHMED A E A, LI Zhenfang. Improved phase gradient autofocus algorithm based on segments of variable lengths and minimum-entropy phase correction[J]. IET Radar, Sonar & Navigation, 2015, 9(4):467-479.
[24]	LI Y, O'YOUNG S. Kalman filter disciplined phase gradient autofocus for stripmap SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(9):6298-6308.
[25]	温雪娇, 仇晓兰, 尤红建, 等. 高分辨率星载SAR起伏运动目标精细聚焦与参数估计方法[J]. 雷达学报, 2017, 6(2):213-220. DOI: 10.12000/JR17005.
	WEN Xuejiao, QIU Xiaolan, YOU Hongjian, et al. Focusing and parameter estimation of fluctuating targets in high resolution spacebone SAR[J]. Journal of Radars, 2017, 6(2):213-220. DOI: 10.12000/JR17005.
[26]	LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324.
[27]	NAIR V, HINTON G E. Rectified linear units improve restricted boltzmann machines[C]//Proceedings of the 27th International Conference on Machine Learning. Haifa: IEEE, 2010: 807-814.
[28]	TAI Y, YANG J, LIU X. Image super-resolution via deep recursive residual network[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Hawaii: IEEE, 2017: 3147-3155.
[29]	BOTTOU L. Stochastic gradient descent tricks[C]//Proceedings of 2012 Neural Networks. Berlin: Springer, 2012.
[30]	KINGMA D P, BA J. Adam: a method for stochastic optimization[C]//Proceedings of the 3th International Conference on Learning Representations. San Diego: [s.n.], 2015.
[31]	LOSHCHILOV I, HUTTER F. Decoupled weight decay regularization[C]//Proceedings of the 7th International Conference on Learning Representations. Vancouver: [s.n.], 2019.

方法	熵	对比度	CPU耗时/s	GPU耗时/s
PGA-ML	9.7982	5.976 8	1 597.15	3 351.76
PGA-LUMV	9.796 5	5.983 6	1 617.23	3 555.88
MEA	9.759 3	6.254 9	4 511.39	938.35
N-MEA^[11]	9.761 3	6.248 9	1 469.83	260.06
VDNN-AF^[19]	9.637 9	7.164 4	3 656.93	156.69
CNN-AF	9.578 0	7.402 3	780.50	47.10

方法	熵	对比度	CPU耗时/s	GPU耗时/s
PGA-ML	9.878 7	4.805 6	566.43	1 112.43
PGA-LUMV	9.875 5	4.831 2	585.19	1 108.25
MEA（0.01）	9.896 0	4.598 1	3 491.33	217.56
MEA（0.1）	9.846 7	5.071 0	3 315.42	217.67
MEA（1）	9.844 8	4.598 1	3 272.45	217.31
MEA（10）	9.845 1	5.096 1	3 304.63	216.28
MEA（100）	10.077 3	3.637 1	3 251.51	214.41
N-MEA^[11]	9.836 0	5.212 5	616.77	38.94
AFnet^[20]	9.852 8	4.993 4	369.67	17.61
PAFnet^[20]	9.852 4	5.000 5	203.03	12.05
VDNN-AF^[19]	9.637 9	7.164 4	1 315.94	52.40
CNN-AF	9.578 0	7.402 3	186.31	17.07