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SAR Ground Moving Target Refocusing by Combining mRe³ Network and TVβ-LSTM

Zhou, Yuanyuan ; Shi, Jun ; Wang, Chen ; Hu, Yao ; Zhou, Zenan ; Yang, Xiaqing ; Zhang, Xiaoling ; Wei, Shunjun

IEEE transactions on geoscience and remote sensing, 2022-01, Vol.60, p.1-14 [Periódico revisado por pares]

New York: IEEE

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  • Título:
    SAR Ground Moving Target Refocusing by Combining mRe³ Network and TVβ-LSTM
  • Autor: Zhou, Yuanyuan ; Shi, Jun ; Wang, Chen ; Hu, Yao ; Zhou, Zenan ; Yang, Xiaqing ; Zhang, Xiaoling ; Wei, Shunjun
  • Assuntos: Artificial neural networks ; Convolutional neural network (CNN) ; ground moving target (GMT) ; Imaging ; Long short-term memory ; Moving targets ; Neural networks ; Radar imaging ; Radar tracking ; refocusing ; SAR (radar) ; shadow tracking ; Smoothing ; Smoothing methods ; Smoothness ; Synthetic aperture radar ; synthetic aperture radar (SAR) ; Target tracking ; Tracking networks ; Trajectory
  • É parte de: IEEE transactions on geoscience and remote sensing, 2022-01, Vol.60, p.1-14
  • Descrição: This article proposes a novel framework by combining a modified real-time recurrent regression (mRe 3 ) network and a newly designed trajectory smoothing long short-term memory (LSTM) network for refocusing the ground moving target (GMT) in the synthetic aperture radar (SAR) image. The mRe 3 network that consists of a convolutional neural network (CNN) backbone and two LSTM modules is designed to track the GMT's shadow in an SAR video. Furthermore, we find that the complex trajectory obtained by the tracking network cannot directly be used for refocusing the GMT because of the estimation error. To address the abovementioned problem, a <inline-formula> <tex-math notation="LaTeX">\beta </tex-math></inline-formula>-order total variation loss-based smoothing LSTM (TV<inline-formula> <tex-math notation="LaTeX">{}^{\beta } </tex-math></inline-formula>-LSTM) is proposed to recover the GMT's trajectory to meet the requirement of refocusing. Besides, the effect of TV<inline-formula> <tex-math notation="LaTeX">{}^{\beta } </tex-math></inline-formula> on the performance of smoothing LSTM is analyzed. By the experiments on simulated and real SAR videos, we find that the mRe 3 has stronger robustness and a better trajectory reconstruction precision compared with the existing tracking methods, especially for the strong interference cases. In addition, the smoothing LSTM can recover the trajectory of the GMT with higher precision and better smoothness. When <inline-formula> <tex-math notation="LaTeX">\beta </tex-math></inline-formula> is set to 3, with the TV<inline-formula> <tex-math notation="LaTeX">{}^{\beta } </tex-math></inline-formula>-LSTM, the center distance error of a recovered complex trajectory can be reduced from 0.82 to 0.782, while its fluctuation can be suppressed from 6 to 1 mm. By using our framework, the focused GMT with bountiful geometrical features can be obtained even for the <inline-formula> <tex-math notation="LaTeX">K_{a} </tex-math></inline-formula>-band SAR.
  • Editor: New York: IEEE
  • Idioma: Inglês
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