DeepSAR-Net: Deep convolutional neural networks for SAR target recognition

In this paper, we propose a new deep artificial neural network architecture for synthetic aperture radar target recognition codenamed DeepSAR-Net. Unlike most existing methods, this approach can learn discriminative features directly from the training data instead of requiring pre-specification or pre-selection by a human designer. Furthermore, our method is adaptable, and it is learning to recognize new targets which do not require lots of time and data resources. Experimental results on the MSTAR dataset show that the DeepSAR-Net framework yield dramatic improvement in SAR ATR compared with the state-of-the-art techniques.