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 a fast SAR target recognition algorithm by combination of two dimensional random convolution features and ensemble extreme learning machines. Firstly, two dimensional random convolution features are extracted, where kernel widths are randomly selected from the kernel width set, and random kernels with different widths are generated based on uniform distribution. Convolution and square pooling operations are performed in the input image to extract random convolution features, and these features are transformed into vectors and combined to form a high-dimensional feature vector. Secondly, random sampling operations based on ensemble learning theory are adopted to perform dimensionality dimension to get a low-dimensional feature vector, and extreme learning machines (ELM), which has the advantages of fast training speed, few adjustable parameters, and good generalization performance, are used to train base classifiers. Finally, majority vote method is adopted to combine the classification results of base classifiers to predict the label of the targets. MSTAR database is used to perform SAR target recognition experiments to verify the performance of the proposed algorithm. The parameters which affect recognition performance greatly are firstly analyzed, including the number of the convolution kernel, the width of the convolution kernel, the number of base classifier, and regularization parameter. It can be concluded that, recognition performance with larger kernel width is higher than that with smaller kernel width, where convolution kernels with small width, such as 3×3, is mostly often used in deep convolution models to perform visible image recognition. Extreme learning machine with small regularization coefficient can achieve good generalization capability and improve recognition performance. SAR target recognition experiments are done under standard operating condition and extended operating conditions, and experimental results demonstrate that, the overall recognition performances for ten-class targets with and without distorted configurations are 95.79% and 97.57%, respectively. Meanwhile, the 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. Finally, the recognition performance compared with state-of-the-art classifiers are presented. 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.