Additional feature CNN based automatic target recognition in SAR image

摘要

In this study, we suggest additional feature based convolutional neural networks (CNN) for synthetic aperture radar automatic target recognition (SAR ATR) performance improvement. Previous SAR ATR researches need preprocessing process or prior information such as pose information due to severe image noise on SAR image. However, since the noise characteristics of the SAR image are different for each acquired image, recognition accuracy may be lowered if the preprocessing process is not performed properly. For this reason, we propose additional feature based CNN architecture which does not need additional preprocessing process or pose information. The proposed method consists of three steps. First, extract more detail information included features and noise reduced features from two CNNs using max-pool and average-pool subsampling operation. Second, the features extracted from the two CNNs are aggregated into a single column vector in order to consider both features in target recognition. Lastly, train proposed CNN architecture using aggregated features and fully-connected layers. MSTAR SAR dataset is used for simulation and confirmed that proposed method can recognize targets more accurately than other methods. Using the proposed method in this study, we can recognize the 10 classes of military targets with accuracy of 94.38% without any additional preprocess or prior information.