Spotlight SAR image recognition based on dual-channel feature map convolutional neural network

Synthetic Aperture Radar (SAR) is widely used in agriculture, remote sensing and many other fields due to its allweather working mode and its excellent penetration. However, the decipherment of synthetic aperture radar imaging is very difficult compared to optical images. This problem is even worse in the SAR target recognition. Although the traditional feature engineering method is helpful for SAR image information content extraction, the effect is not satisfied with the requirements in practice. Convolutional neural network is an effective method to extract synthetic aperture radar imaging features and recognize targets. In this paper, a dualchannel feature map convolutional neural network (DCFM-CNN) is proposed, using two different down sampling methods, - pooling and convolution, to extract features for SAR image automatic target recognition (SAR-ATR). An average recognition accuracy of 99.45% was achieved on MSTAR public data set. Preprocessing of synthetic aperture radar imaging is not needed here, and the target recognition is completed by the CNN model. The proposed object recognition approach is effective with low overhead.