High‐quality interferometric inverse synthetic aperture radar imaging using deep convolutional networks

Abstract

AbstractIn 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.