@inproceedings{902b7c33f6be43ddb003dba93030858e,
title = "CV-MotionNet: Complex-Valued Convolutional Neural Network for SAR Moving Ship Targets Classification",
abstract = "In the synthetic aperture radar (SAR) images, moving ship targets are defocused due to the movement, which leads to the problem of poor classification accuracy. Therefore, this paper proposes an amplitude-phase-type complex-valued convolutional neural network (AP-CV-CNN) architecture called CV-MotionNet to classify SAR moving ship targets without motion compensation. It utilizes both amplitude and phase information of complex SAR images. CV-MotionNet uses amplitude-phase-type activation function to processing amplitude and phase information more conducive. Then, the proposed CV-MotionNet is tested on simulated five-types SAR moving ship target classification task and GF-3 SAR ship classification. Simulation and experiment show that the classification error can be further reduced if using CV-MotionNet instead of real-valued CNN (RV-CNN) with the same degree of freedom.",
keywords = "Complex-valued convolutional neural network (CV-CNN), moving ship, synthetic aperture radar (SAR), target classification",
author = "Yun Zhang and Qinglong Hua and Yicheng Jiang and Hongbo Li and Dan Xu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 ; Conference date: 12-07-2021 Through 16-07-2021",
year = "2021",
doi = "10.1109/IGARSS47720.2021.9554116",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4280--4283",
booktitle = "IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",
address = "美国",
}