Convolutional Neural Network (CNN) models applied to synthetic aperture radar automatic target recognition (SAR ATR) universally focus on two important issues: overfitting caused by lack of sufficient training data and independent variations like worse estimates of the aspect angle etc. To this end, we developed a lightweight CNN-based method named SARNet to accomplish the classification task. Firstly, a clock-wise data amplification approach is presented to generate adequate SAR images without requiring many raw SAR images, effectively avoiding overfitting in the course of training. Then a SARNet is devised to process the extracted features from SAR target images and work on classification tasks with parameters fine-tuning under comparative models. To enhance and structurally organize the representation of learned proposed model, various activation functions are explored in this paper. Furthermore, due to the pioneering conducted experiments, training samples in the MSTAR and extended MSTAR database are utilized to demonstrate the robustness and effectiveness of the lightweight model. Experimental results have shown that our proposed model has achieved a 98.30% state-of-the-art accuracy.
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