中文摘要：

该文提出了一种基于栈式自编码器（Stacked Auto Encoder,SAE）特征融合的合成孔径雷达（Synthetic Aperture Rader,SAR）图像车辆目标识别算法。首先,该算法提取了SAR图像的25种基线特征（baseline features）和局部纹理特征（Three-Patch Local Binary Patterns,TPLBP）。然后将特征串联输入SAE网络中进行融合,采用逐层贪婪训练法对网络进行预训练。最后利用softmax分类器微调网络,提高网络融合性能。另外,该文提取了SAR图像的Gabor纹理特征,进行了不同特征之间的融合实验。结果表明基线特征与TPLBP特征冗余性小,互补性好,融合后的特征区分性大。与直接利用SAE,CNN（Convolutional Neural Network）进行目标识别的算法相比,基于SAE的特征融合算法简化了网络结构,提高了识别精度与识别效率。基于MSTAR数据集的10类目标分类精度达95.88%,验证了算法的有效性。

英文摘要：

A feature fusion algorithm based on a Stacked Auto Encoder（SAE）for Synthetic Aperture Rader（SAR）imagery is proposed in this paper.Firstly,25 baseline features and Three-Patch Local Binary Patterns（TPLBP）features are extracted.Then,the features are combined in series and fed into the SAE network,which is trained by a greedy layer-wise method.Finally,the softmax classifier is employed to fine tune the SAE network for better fusion performance.Additionally,the Gabor texture features of SAR images are extracted,and the fusion experiments between different features are carried out.The results show that the baseline features and TPLBP features have low redundancy and high complementarity,which makes the fused feature more discriminative.Compared with the SAR target recognition algorithm based on SAE or CNN（Convolutional Neural Network）,the proposed method simplifies the network structure and increases the recognition accuracy and efficiency.10-classes SAR targets based on an MSTAR dataset got a classification accuracy up to 95.88%,which verifies the effectiveness of the presented algorithm.