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

Sensors

Fabio Scotti¹  RuggeroDonida Labati¹  Vincenzo Piuri¹  Faguan Wang²  Chen Xuan²  Wenbo Deng²  Chaoyun Mai²  Yikui Zhai²  Zilu Ying²  Jingwen Li³  Bing Sun³ 

[1] Departimento di Information, Universita, Degli Studi di Milano, via Celoria 18, 20133 Milano (MI), Italy;Department of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China;School of Electronics and Information Engineering, Beihang University, Beijing 100191, China;

关键词: synthetic aperture radar (sar);    convolutional neural network (cnn);    transfer learning;    atrous-inception module;    lightweight network;    small sample;   

DOI  :  10.3390/s20061724

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown