合成孔径雷达（Synthetic Aperture Radar，SAR）图像目标识别是SAR图像解译的重要应用。为提高SAR目标识别的稳健性，本文提出基于深度信念网络（Deep Belief Network，DBN）的属性散射中心匹配方法。属性散射中心参数特征丰富，能够很好地反映目标的局部散射特性。DBN发挥深度学习优势，可以实现测试样本与模板样本散射中心集的稳健匹配，并且能够较好地适应噪声干扰、部分缺失等情形。在构建的属性散射中心匹配关系的基础上，定义相似度度量准则。基于最大相似度的原则确定测试样本所属类别。实验依托MSTAR数据集开展，经验证，所提方法对于SAR目标识别问题具有良好的有效性和稳健性。

Synthetic aperture radar (SAR) image target recognition is an important application of SAR image interpretation. In order to improve the robustness of SAR target recognition, this paper proposed an attribute scattering center matching method based on deep belief network (DBN). The attribute scattering center had rich parameters, which could well reflect the local scattering characteristics of the target. DBN took advantage of deep learning to achieve robust matching between the scattering center sets from test samples and template samples, which could also better adapt to noise interference, partial absence and other situations. Based on the matching correspondence of the attribute scattering center sets, the similarity measure criterion was defined. The target label of the test sample was determined based on the principle of the maximum similarity. Experiments were carried out based on MSTAR dataset, and the proposed method was proved to be effective and robust for SAR target recognition.