Premium
High‐quality interferometric inverse synthetic aperture radar imaging using deep convolutional networks
Author(s) -
Zhang Ye,
Yang Qi,
Zeng Yang,
Deng Bin,
Wang Hongqiang,
Qin Yuliang
Publication year - 2020
Publication title -
microwave and optical technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.304
H-Index - 76
eISSN - 1098-2760
pISSN - 0895-2477
DOI - 10.1002/mop.32411
Subject(s) - computer science , convolutional neural network , inverse synthetic aperture radar , radar imaging , interferometry , synthetic aperture radar , artificial intelligence , radar , image quality , computer vision , optics , image (mathematics) , telecommunications , physics
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.