TY - GEN
T1 - Refocusing of Ship Target under Three-Dimensional Rotating in SAR Based on Complex-Valued Deep Learning
AU - Zhang, Yun
AU - Hua, Qinglong
AU - Wei, Chenxi
AU - Wang, Haotian
AU - Jiang, Yicheng
AU - Xu, Dan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In synthetic aperture radar (SAR) images, ship targets are defocused due to three-dimensional rotation, which affects subsequent SAR target detection and recognition tasks. This paper proposes a complex-valued convolutional neural network (CV-CNN) structure called CV-RefocusNet to refocus SAR three-dimensional rotating ship targets. CV-RefocusNet includes two parts of feature extraction network and image reconstruction network and adopts an end-to-end design method. To make full use of the amplitude and phase information of complex SAR images, the convolutional layer, deconvolutional layer, and activation function in CV-RefocusNet are all extended to the complex domain. Then refocusing experiments on simulated SAR data and GF-3 SAR data show that CV-RefocusNet could further improve the focus accuracy instead of real-value CNN (RV-CNN) with the same degree of freedom.
AB - In synthetic aperture radar (SAR) images, ship targets are defocused due to three-dimensional rotation, which affects subsequent SAR target detection and recognition tasks. This paper proposes a complex-valued convolutional neural network (CV-CNN) structure called CV-RefocusNet to refocus SAR three-dimensional rotating ship targets. CV-RefocusNet includes two parts of feature extraction network and image reconstruction network and adopts an end-to-end design method. To make full use of the amplitude and phase information of complex SAR images, the convolutional layer, deconvolutional layer, and activation function in CV-RefocusNet are all extended to the complex domain. Then refocusing experiments on simulated SAR data and GF-3 SAR data show that CV-RefocusNet could further improve the focus accuracy instead of real-value CNN (RV-CNN) with the same degree of freedom.
KW - CV-RefocusNet
KW - Complex-valued convolutional neural network (CV-CNN)
KW - deep learning
KW - synthetic aperture radar (SAR)
KW - threedimensional rotation
UR - https://www.scopus.com/pages/publications/85140408108
U2 - 10.1109/IGARSS46834.2022.9884213
DO - 10.1109/IGARSS46834.2022.9884213
M3 - 会议稿件
AN - SCOPUS:85140408108
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 2303
EP - 2306
BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Y2 - 17 July 2022 through 22 July 2022
ER -