合成孔径雷达（synthetic aperture radar，SAR）自动目标识别过程主要包括目标特征提取和分类器训练两个步骤。提出一种基于深度卷积神经网络（deep convolutional neural networks，DNNs）的SAR自动目标识别方法，使用一类优化的DNNs网络结构对SAR图像目标进行分类训练。该网络结构自动提取目标类别特征，避免人工预选取特征方法带来的不标准性。在DNNs网络模型训练过程中引入迁移学习的概念，以防止结果陷入局部最优解和加快模型参数的训练。最后使用美国运动和静止目标获取与识别MSTAR数据集进行试验，给出该方法与其他分类方法结果的对比，证明其取得较高的分类正确率。
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