Abstract
Deep Convolutional Neural Networks (DCNNs) have been widely used in target recognition due to the availability of large dataset. The DCNNs have the ability of learning highly hierarchical image feature, which provides great opportunity for synthetic aperture radar automatic target recognition (SAR-ATR). However, when the DCNNs were directly applied to the SAR target recognition, it will result in severe overfitting due to limited SAR image training data. To overcome this problem, we present a Gabor-Deep Convolutional Neural Networks (G-DCNNs). Instead of training a deep network with limited dataset of raw SAR images, Gabor features for multi -scale and multi -direction were used for data augmentation as training dataset at first. Then based on this data augmentation method, we designed a DCNNs for SAR image target recognition. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset prove the effectiveness of our method.
Published Version
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