Change Detection of Foliage-concealed Targets Based on Deep Neural Network in Low Frequency Ultra-wideband SAR Images-Reference-Cited by-同舟云学术

Change Detection of Foliage-concealed Targets Based on Deep Neural Network in Low Frequency Ultra-wideband SAR Images

Published:2021-11-21 Issue: Volume: Page:
ISSN:
Container-title:2021 Photonics & Electromagnetics Research Symposium (PIERS)
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Author:

Xie Hongtu¹,Chen Kaipeng¹,Xie Ni²,Liang Kang¹,Jiang Xinqiao¹,Wang Guoqian³

Affiliation:

1. Sun Yat-sen University,School of Electronics and Communication Engineering,Guangzhou,China,510275

2. Hunan University of Science and Technology,School of Business,Xiangtan,China,411201

3. Sun Yat-sen University,Sun Yat-sen Memorial Hospital,Guangzhou,China,510120

Funder

National Natural Science Foundation of China

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9694620/9694666/09694856.pdf?arnumber=9694856

Reference16 articles.

1. ROI extract method for UWB SAR foliage-concealed target detection;yang;Systems Engineering and Electronics,2007

2. New target detection method for FOPEN UWB SAR;fang;Systems Engineering and Electronics,2005

3. Foliage-concealed target detection based on deep learning in low frequency UWB SAR images;xie;Proceedings of CIE International Conference on Radar (RADAR),2021

4. False alarm reduction in wavelength-resolution SAR change detection schemes by using a convolutional neural network;campos;IEEE Geoscience and Remote Sensing Letters,2020

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1. Foliage-concealed Target Detection Based on Deep Learning in Low Frequency UWB SAR Images;2021 CIE International Conference on Radar (Radar);2021-12-15