中文摘要：

该文研究了训练样本不足的情况下利用卷积神经网络（Convolutional Neural Network, CNN）对合成孔径雷达（SAR）图像实现目标检测的问题。利用已有的完备数据集来辅助场景复杂且训练样本不足的数据集进行检测。首先用已有的完备数据集训练得到CNN分类模型，用于对候选区域提取网络和目标检测网络做参数初始化；然后利用完备数据集对训练数据集做扩充；最后通过“四步训练法”得到候选区域提取模型和目标检测模型。实测数据的实验结果证明，所提方法在SAR图像目标检测中可以获得较好的检测效果。

英文摘要：

This paper studies the issue of SAR target detection with CNN when the training samples are insufficient. The existing complete dataset is employed to assist accomplishing target detection task, where the training samples are not enough and the scene is complicated. Firstly, the existing complete dataset with image-level annotations is used to pre-train a CNN classification model, which is utilized to initialize the region proposal network and detection network. Then, the training dataset is enlarged with the existing complete dataset. Finally, the region proposal model and detection model are obtained through the pragmatic “4-step training algorithm” with the augmented training dataset. The experimental results on the measured data demonstrate that the proposed method can improve the detection performance compared with the traditional detection methods.