Open AccessMF‐SarNet: Effective CNN with data augmentation for SAR automatic target recognition
Author(s) -
Zhai Yikui,
Ma Hui,
Cao He,
Deng Wenbo,
Liu Jian,
Zhang Zhongyi,
Guan Huixin,
Zhi Yihang,
Wang Jinxing,
Zhou Jihua
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0218
Subject(s) - computer science , artificial intelligence , automatic target recognition , pattern recognition (psychology) , speech recognition , computer vision , synthetic aperture radar
An effective Max‐Fire CNN model MF‐SarNet for synthetic aperture radar (SAR) automatic target recognition (ATR) is presented, here. By selecting the convolution kernel of the Fire module in the network, the parameters are reduced to obtain the effective convolutional neural network of less parameter. In view of the requirement of deep learning for large‐scale data, an augmentation method is proposed, which can learn the features of large database better. The results based on MSTAR database show that the model is effective and the result is encouraging. The accuracy of SAR image recognition is 98.53%.