University of Oulu

He Q., Zhao L., Kuang G., Liu L. (2021) SAR Target Recognition Based on Model Transfer and Hinge Loss with Limited Data. In: Fang L., Chen Y., Zhai G., Wang J., Wang R., Dong W. (eds) Artificial Intelligence. CICAI 2021. Lecture Notes in Computer Science, vol 13069. Springer, Cham.

SAR target recognition based on model transfer and hinge loss with limited data

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Author: Qishan He1; Zhao, Lingjun1; Kuang, Gangyao1;
Organizations: 1State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System,College of Electronics Science and Technology, National University of Defense Technology, Changsha, China
2College of System Engineering, National University of Defense Technology, China
3CMVS, Univeristy of Oulu, Finland
Format: article
Version: accepted version
Access: embargoed
Persistent link:
Language: English
Published: Springer Nature, 2021
Publish Date: 2023-01-01


Convolutional neural networks have made great achievements in field of optical image classification during recent years. However, for Synthetic Aperture Radar automatic target recognition (SAR-ATR) tasks, the performance of deep learning networks is always degraded by the insufficient size of SAR images, which cause both severe over-fitting and low-capacity feature extraction model. On the other hand, models with high feature representation ability usually lose anti-overfitting capability to a certain extent, while enhancing the network’s robustness leads to degradation in feature extraction capability. To balance above both problems, a network with model transfer using the GAN-WP and non-greedy loss is introduced in this paper. Firstly, inspired by the Support Vector Machine’s mechanism, multi-hinge loss is used during training stage. Then, instead of directly training a deep neural network with the insufficient labeled SAR dataset, we pretrain the feature extraction network by an improved GAN, called Wasserstein GAN with gradient penalty and transfer the pre-trained layers to an all-convolutional network based on the fine-tune technique. Furthermore, experimental results on the MSTAR dataset illustrate the effectiveness of the proposed new method, which additional shows the classification accuracy can be improved more largely than other method in the case of sparse training dataset.

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Series: Lecture notes in computer science
ISSN: 0302-9743
ISSN-E: 1611-3349
ISSN-L: 0302-9743
ISBN: 978-3-030-93046-2
ISBN Print: 978-3-030-93045-5
Volume: 13069
Pages: 191 - 201
DOI: 10.1007/978-3-030-93046-2_17
Host publication: Artificial Intelligence. CICAI 2021
Host publication editor: Fang, Lu
Chen, Yiran
Zhai, Guangtao
Wang, Jane
Wang, Ruiping
Dong, Weisheng
Conference: CAAI International Conference on Artificial Intelligence
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Copyright information: © Springer Nature Switzerland AG 2021. This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at