基于卷积神经网络的快速SAR自聚焦方法

Fast SAR autofocus based on convolutional neural networks

自聚焦是SAR高分辨率成像的关键技术。然而,传统的SAR自聚焦方法均需要迭代多次,实时性差,不适合在轨处理。本文提出了一种基于卷积神经网络的在轨快速SAR自聚焦方法(CNN-AF),该方法采用卷积神经网络来学习失焦图像到聚焦图像的映射,主要用于校正方位向的相位误差,由于在测试阶段该方法无须迭代和调整参数,因此该方法实时性好,更加适用于在轨处理。在真实SAR数据上的试验结果表明,本文方法具有较高的聚焦质量和聚焦速度。

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.