In this article, a modified complex‐valued convolutional neural network (MCV‐CNN) specifically for interferometric inverse synthetic aperture radar (InISAR) imaging is proposed. Comparing with the fast Fourier transformation‐based and sparsity‐driven imaging algorithms, the MCV‐CNN can achieve super‐resolution and side‐lobe suppression on the imaging results simultaneously within a short time. The inputs of the MCV‐CNN are complex‐valued radar echo data, and the outputs are complex‐valued ISAR images which contain both the amplitude and phase information. Then the phase information is adopted to perform an interferometric operation, and the high‐quality three‐dimensional InISAR imaging results can be achieved. A 0.22 THz InISAR imaging experiment has been carried out to show the superiority of the proposed method on imaging quality and computational efficiency.

中文翻译：

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使用深度卷积网络的高质量干涉逆合成孔径雷达成像

在本文中，提出了一种改进的复数值卷积神经网络（MCV-CNN），专门用于干涉式逆合成孔径雷达（InISAR）成像。与基于快速傅里叶变换和稀疏驱动的成像算法相比，MCV-CNN可以在短时间内同时对成像结果实现超分辨率和旁瓣抑制。MCV-CNN的输入是复数值雷达回波数据，输出是复数值ISAR图像，其中包含幅度和相位信息。然后采用相位信息进行干涉测量，即可获得高质量的3维InISAR成像结果。A 0。