深度卷积神经网络在目标检测与识别等方面表现出了优异性能，但将其用于SAR 目标识别时，较少的训练样本和深度模型的优化设计是必须解决的两个问题。本文设计了一种结合二维随机卷积特征和集成超限学习机的SAR 目标识别算法。首先，随机生成具有不同宽度的二维卷积核，对输入图像进行卷积与池化操作，提取随机卷积特征向量。其次，为提高分类器的泛化能力，并降低训练时间，基于集成学习思想对提取的卷积特征进行随机采样，然后采用超限学习机训练基分类器。最后，通过投票表决法对基分类器的分类结果进行集成。采用MSTAR 数据集进行了SAR 目标识别实验，实验结果表明，由于采用的超限学习机具有快速训练能力，训练时间降低了数十倍，在无需进行数据增强的情况下，分类精度与采用数据增强和多层卷积神经网络的深度学习算法相当。提出的算法具有实现简单、需要调整参数少等优点，采用集成学习思想提高了分类器的泛化能力。

Deep convolution neural network has demonstrated excellent performance in target detection and recognition tasks. However, few training samples and optimization design of deep models are two main problems to be solved when applied to SAR target recognition. This paper proposes an algorithm for SAR target recognition by combination of two dimensional random convolution features and ensemble extreme learning machines. Firstly, two dimensional random convolution kernels with different widths are generated, and convolution and pooling operations are performed in input image to extract random convolution feature vectors. Secondly, random samplings based on ensemble learning theory are done for extracted feature vectors to improve generalization performance of classifier and reduce training time, and base classifiers are then trained by extreme learning machines (ELM). Finally, majority vote method is adopted to combine the classification results of base classifiers. SAR target recognition experiments based on MSTAR database were performed, and experimental results demonstrate that, training time has dropped by ten times due to fast training capability of ELM, and the proposed algorithm achieves comparable classification performance with deep-learning-based methods which use data augmentation and multiple convolution layers. The proposed algorithm has the advantages of easy implementation and fewer adjustable parameters, and improves classifier’ s generalization performance through adoption of ensemble learning ideas.