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.

Figures/Tables 9

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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