MF-SarNet: Effective CNN with data augmentation for SAR automatic target recognition

Abstract

An effective Max-Fire CNN model MF-SarNet for synthetic aperture radar (SAR) automatic target recognition (ATR) is presented, here. By selecting the convolution kernel of the Fire module in the network, the parameters are reduced to obtain the effective convolutional neural network of less parameter. In view of the requirement of deep learning for large-scale data, an augmentation method is proposed, which can learn the features of large database better. The results based on MSTAR database show that the model is effective and the result is encouraging. The accuracy of SAR image recognition is 98.53%.

Keywords

• radar target recognition
• image recognition
• radar imaging
• synthetic aperture radar
• learning (artificial intelligence)
• convolutional neural nets
• synthetic aperture radar automatic target recognition
• convolution kernel
• Fire module
• SAR image recognition
• data augmentation
• SAR automatic target recognition
• convolutional neural network
• Max-Fire CNN model
• MF-SarNet
• ATR
• MSTAR database
• deep learning