Open AccessSAR ATR with full‐angle data augmentation and feature polymerisation
Author(s) -
Zhai Yikui,
Ma Hui,
Liu Jian,
Deng Wenbo,
Shang Lijuan,
Sun Bing,
Jiang Ziyi,
Guan Huixin,
Zhi Yihang,
Wu Xi,
Zhou Jihua
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0219
Subject(s) - automatic target recognition , synthetic aperture radar , artificial intelligence , computer science , convolutional neural network , feature (linguistics) , pattern recognition (psychology) , feature extraction , artificial neural network , rotation (mathematics) , computer vision , philosophy , linguistics
Utilising neural networks to learn and extract valuable features has achieved satisfactory performance for synthetic aperture radar automatic target recognition (SAR ATR). However, such target recognition capability could be seriously limited by severe image rotation. To greatly improve the performance of convolutional neural networks‐based SAR ATR, a data augmentation method combining region of interest (ROI) extraction and full‐angle rotation method is firstly proposed in this study. Then, an inception‐SAR NET is presented to polymerise multi‐branch feature maps. The superior performance of inception‐SAR NET structures is obtained by comprehensive experiments. Finally, the results based on MSTAR dataset demonstrate that authors’ methods could achieve the most advanced performance.