一种基于GAN的快速聚焦方法,用于循环SAR图像
Bingxuan Li1, Yanheng Ma1, Lina Chu1
1Army Engineering University Shijiazhuang Campus, Shijiazhuang, 050000, Hebei, China.
Heliyon
|August 5, 2024
概括
本研究介绍了一种新的生成对抗网络 (GAN) 方法,用于循环合成孔径雷达 (CSAR) 图像聚焦. 该方法通过直接聚焦亚光圈图像来提高效率和准确性,克服了传统技术的局限性.
科学领域:
- 遥感 遥感 遥感 遥感
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 循环合成孔径雷达 (CSAR) 成像易受大气和运动错误的影响,导致相位偏移和运动错误.
- 传统的时间域相位补偿方法,如自动回归向后投影 (ARBP),是计算密集的,需要逐方向处理和大量资源.
研究的目的:
- 开发一种更有效,更准确的方法来聚焦CSAR图像.
- 为了解决现有的CSAR聚焦技术的计算和内存限制.
主要方法:
- 一种使用生成对抗网络 (GAN) 直接聚焦CSAR亚光圈图像的新方法.
- 引入自动对焦频率损失 (AFFL) 以减轻网络的低频偏差.
- 关于焦点位置特征注意力 (FPFA) 的建议,以提高焦点位置提取精度.
- 实施新的融合策略,用于后聚焦亚光圈图像.
主要成果:
- 实验验证表明,CSAR图像聚焦效率显著改善.
- 拟议的方法在CSAR图像聚焦中实现了更高的精度.
- 基于GAN的方法有效地克服了传统时间域方法的局限性.
结论:
- 这种基于GAN的新方法为CSAR图像聚焦提供了优质的替代方案.
- 整合AFFL和FPFA有助于提高对焦精度和稳定性.
- 这种方法在合成光圈雷达成像处理方面呈现出有前途的进步.
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