Multiple Feature Aggregation Using Convolutional Neural Networks for SAR Image-Based Automatic Target Recognition

Since synthetic aperture radar (SAR) images contain severe noise, it is important to extract noise excluded feature when recognizing the target in the SAR image. Therefore, previous SAR automatic target recognition (ATR) methods use separate preprocessing process or pose information of SAR to reduce the influence of noise. However, since noise characteristics of SAR images are different from image to image, recognition accuracy cannot be guaranteed if the preprocessing process is conducted improperly or there is no pose information. For this reason, we propose multiple feature-based convolutional neural networks (MFCNNs) recognizing the target of the SAR image without using a separate preprocessing process or pose information. MFCNN consists of three steps. First, extract strong features of the target with more effect of noise and smoothed features with lesser effect of noise. Second, aggregate extracted features with complementary relationships into a single column vector. Last, fully connected networks recognize the target using aggregated features. We used moving and stationary target acquisition and recognition SAR public data set for simulations and confirmed that the proposed method can recognize the target of the SAR image more accurately than previous SAR ATR methods without preprocessing process and pose information.