SAR ATR based on displacement- and rotation-insensitive CNN

ABSTRACT

Among many synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms, convolutional neural network (CNN)-based algorithms are the commonly used methods. However, most previous SAR ATR studies assume that the precise location (and heading direction) of a target is (are) known and image is not suffering from translations, which are not always true in realistic applications. In this letter, a modern CNN model is trained by samples with no rotation and displacement, and is evaluated on the dataset with rotation and displacement. The results show that the classification accuracy is very low when the target’s displacement or rotation angle is different from the pre-assumed value in the training dataset. To overcome this problem, a displacement- and rotation-insensitive deep CNN is trained by augmented dataset. The proposed method is evaluated on moving and stationary target acquisition and recognition (MSTAR) dataset. It proves that our proposed method could achieve high accuracy in all three subsets which have different displacement and rotation settings.

Acknowledgements

The authors hereby extend their grateful thanks to Professor Timothy Warner and the anonymous reviewers for their constructive suggestions that are very helpful for the authors to improve this letter.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported in part by the National Natural Science Foundation of China under Grant [61301025], and in part by the Hundred Talents Program of the Chinese Academy of Sciences.