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Volume 20
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10.3390/s20061724
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Article

TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR

by
Zilu Ying
1,
Chen Xuan
1,
Yikui Zhai
1,*,
Bing Sun
2,
Jingwen Li
2,
Wenbo Deng
1,
Chaoyun Mai
1,
Faguan Wang
1,
Ruggero Donida Labati
3,
Vincenzo Piuri
3 and
Fabio Scotti
3
1
Department of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China
2
School of Electronics and Information Engineering, Beihang University, Beijing 100191, China
3
Departimento di Information, Universita, Degli Studi di Milano, via Celoria 18, 20133 Milano (MI), Italy
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1724; https://doi.org/10.3390/s20061724
Submission received: 10 February 2020 / Revised: 12 March 2020 / Accepted: 14 March 2020 / Published: 19 March 2020
(This article belongs to the Section Remote Sensors)
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Abstract

Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the traditional deep learning models are difficult to effectively extract key features of the targets and share high computational complexity. To solve the problem, an effective lightweight Convolutional Neural Network (CNN) model incorporating transfer learning is proposed for better handling SAR targets recognition tasks. In this work, firstly we propose the Atrous-Inception module, which combines both atrous convolution and inception module to obtain rich global receptive fields, while strictly controlling the parameter amount and realizing lightweight network architecture. Secondly, the transfer learning strategy is used to effectively transfer the prior knowledge of the optical, non-optical, hybrid optical and non-optical domains to the SAR target recognition tasks, thereby improving the model’s recognition performance on small sample SAR target datasets. Finally, the model constructed in this paper is verified to be 97.97% on ten types of MSTAR datasets under standard operating conditions, reaching a mainstream target recognition rate. Meanwhile, the method presented in this paper shows strong robustness and generalization performance on a small number of randomly sampled SAR target datasets.
Keywords: Synthetic Aperture Radar (SAR); Convolutional Neural Network (CNN); transfer learning; Atrous-Inception module; lightweight network; small sample Synthetic Aperture Radar (SAR); Convolutional Neural Network (CNN); transfer learning; Atrous-Inception module; lightweight network; small sample

Share and Cite

MDPI and ACS Style

Ying, Z.; Xuan, C.; Zhai, Y.; Sun, B.; Li, J.; Deng, W.; Mai, C.; Wang, F.; Labati, R.D.; Piuri, V.; et al. TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR. Sensors 2020, 20, 1724. https://doi.org/10.3390/s20061724

AMA Style

Ying Z, Xuan C, Zhai Y, Sun B, Li J, Deng W, Mai C, Wang F, Labati RD, Piuri V, et al. TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR. Sensors. 2020; 20(6):1724. https://doi.org/10.3390/s20061724

Chicago/Turabian Style

Ying, Zilu, Chen Xuan, Yikui Zhai, Bing Sun, Jingwen Li, Wenbo Deng, Chaoyun Mai, Faguan Wang, Ruggero Donida Labati, Vincenzo Piuri, and et al. 2020. "TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR" Sensors 20, no. 6: 1724. https://doi.org/10.3390/s20061724

APA Style

Ying, Z., Xuan, C., Zhai, Y., Sun, B., Li, J., Deng, W., Mai, C., Wang, F., Labati, R. D., Piuri, V., & Scotti, F. (2020). TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR. Sensors, 20(6), 1724. https://doi.org/10.3390/s20061724

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