RESEARCH on target detection of SAR images based on deep learning

In this paper the target detection based on deep convolution neural network (DCNN) and transfer learning has been developed for synthetic aperture radar (SAR) images inspired by recent successful deep learning methods. DCNN has excellent performance in optical images, while its application for SAR images is restricted by the limited quantity of SAR imagery training data. Transfer learning has been introduced into the target detection of a small quantity of SAR images. Firstly, by some contrast experiments to transfer convolution weights layer by layer and analyze its impact, the combination of fine-tuned and frozen weights is used to improve the generalization and stability of the network. Then, the network model is improved according to the target detection task, it increases the network detection speed and reduces the network parameters. Finally, combining with the complicated scene clutter slices to train the network, the false alarm targets number of background clutter is reduced. The detection results of complex multi-target scenes show that the proposed method has good generality while ensuring good detection performance.