High-Performance SAR Automatic Target Recognition Under Limited Data Condition Based on a Deep Feature Fusion Network

Abstract

Deep learning algorithms have been widely applied to the recognition of remote-sensing images due to their excellent performance on various recognition problems with sufficient data. However, limited data on synthetic aperture radar (SAR) images degrade the performance of neural networks for SAR automatic target recognition (ATR). To address this problem, this paper presents a new deep feature fusion framework by combining the Gabor features and information of raw SAR images and fusing the feature vectors extracted from different layers in our proposed neural network. Gabor features improve the richness of SAR image features. The number of free parameters of neural networks is largely reduced by utilizing large-scale convolutional kernel factorization and global average pooling. Moreover, the fusion of feature vectors from different layers helps improve the recognition performance of neural networks. Experimental results on the MSTAR dataset demonstrate the effectiveness of our proposed method. The proposed neural network can achieve an average accuracy of 99% on the classification of ten-class targets and even achieve a high recognition accuracy on limited data and noisy data.