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RESEARCH on target detection of SAR images based on deep learning

机译：基于深度学习的SAR图像目标检测研究

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摘要

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.
机译：在本文中，基于深度卷积神经网络（DCNN）和转移学习的目标检测技术已经发展为受最近成功的深度学习方法启发的合成孔径雷达（SAR）图像。 DCNN在光学图像中具有出色的性能，而其在SAR图像中的应用受到数量有限的SAR图像训练数据的限制。转移学习已被引入到少量SAR图像的目标检测中。首先，通过一些对比实验，逐层传递卷积权重并分析其影响，结合使用微调权重和冻结权重来提高网络的泛化性和稳定性。然后，根据目标检测任务对网络模型进行改进，提高了网络检测速度，减少了网络参数。最后，结合复杂的场景杂波切片训练网络，减少了背景杂波的虚假目标。复杂多目标场景的检测结果表明，该方法具有良好的通用性，同时又能保证良好的检测性能。

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