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Article

Multi-Aspect SAR Target Recognition Based on Prototypical Network with a Small Number of Training Samples

by 1,2,3, 1,2,3,*, 4, 1,3 and 1,2,3
1
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100194, China
2
University of Chinese Academy of Sciences, Beijing 100190, China
3
Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences, Beijing 100194, China
4
Beijing Institute of Remote Sensing Information, Beijing 100192, China
*
Author to whom correspondence should be addressed.
Academic Editors: Vito Pascazio, Mengdao Xing, Gianfranco Fornaro and Hanwen Yu
Sensors 2021, 21(13), 4333; https://doi.org/10.3390/s21134333
Received: 11 May 2021 / Revised: 11 June 2021 / Accepted: 22 June 2021 / Published: 24 June 2021
At present, synthetic aperture radar (SAR) automatic target recognition (ATR) has been deeply researched and widely used in military and civilian fields. SAR images are very sensitive to the azimuth aspect of the imaging geomety; the same target at different aspects differs greatly. Thus, the multi-aspect SAR image sequence contains more information for classification and recognition, which requires the reliable and robust multi-aspect target recognition method. Nowadays, SAR target recognition methods are mostly based on deep learning. However, the SAR dataset is usually expensive to obtain, especially for a certain target. It is difficult to obtain enough samples for deep learning model training. This paper proposes a multi-aspect SAR target recognition method based on a prototypical network. Furthermore, methods such as multi-task learning and multi-level feature fusion are also introduced to enhance the recognition accuracy under the case of a small number of training samples. The experiments by using the MSTAR dataset have proven that the recognition accuracy of our method can be close to the accruacy level by all samples and our method can be applied to other feather extraction models to deal with small sample learning problems. View Full-Text
Keywords: synthetic aperture radar (SAR); automatic target recognition (ATR); multi-aspect SAR; prototypical network; small number of training sample synthetic aperture radar (SAR); automatic target recognition (ATR); multi-aspect SAR; prototypical network; small number of training sample
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MDPI and ACS Style

Zhao, P.; Huang, L.; Xin, Y.; Guo, J.; Pan, Z. Multi-Aspect SAR Target Recognition Based on Prototypical Network with a Small Number of Training Samples. Sensors 2021, 21, 4333. https://doi.org/10.3390/s21134333

AMA Style

Zhao P, Huang L, Xin Y, Guo J, Pan Z. Multi-Aspect SAR Target Recognition Based on Prototypical Network with a Small Number of Training Samples. Sensors. 2021; 21(13):4333. https://doi.org/10.3390/s21134333

Chicago/Turabian Style

Zhao, Pengfei, Lijia Huang, Yu Xin, Jiayi Guo, and Zongxu Pan. 2021. "Multi-Aspect SAR Target Recognition Based on Prototypical Network with a Small Number of Training Samples" Sensors 21, no. 13: 4333. https://doi.org/10.3390/s21134333

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