Fast SAR target recognition based on random convolution features and ensemble extreme learning machines

Abstract

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.