Citation: | Gu Yu, Xu Ying. Fast SAR target recognition based on random convolution features and ensemble extreme learning machines[J]. Opto-Electronic Engineering, 2018, 45(1): 170432. doi: 10.12086/oee.2018.170432 |
[1] | El-darymli K, Gill E W, Mcguire P, et al. Automatic target recognition in synthetic aperture radar imagery: a state-of-the-art review[J]. IEEE Access, 2016, 4: 6014–6058. doi: 10.1109/ACCESS.2016.2611492 |
[2] | Pei J F, Huang Y L, Huo W B, et al. SAR imagery feature extraction using 2DPCA-based two-dimensional neighborhood virtual points discriminant embedding[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(6): 2206–2214. doi: 10.1109/JSTARS.2016.2555938 |
[3] | Amoon M, Rezai-Rad G A. Automatic target recognition of synthetic aperture radar (SAR) images based on optimal selection of Zernike moments features[J]. IET Computer Vision, 2014, 8(2): 77–85. doi: 10.1049/iet-cvi.2013.0027 |
[4] | Du P J, Samat A, Gamba P, et al. Polarimetric SAR image classification by boosted multiple-kernel extreme learning machines with polarimetric and spatial features[J]. International Journal of Remote Sensing, 2014, 35(23): 7978–7990. doi: 10.1080/2150704X.2014.978952 |
[5] | Zhao Q, Principe J C. Support vector machines for SAR automatic target recognition[J]. IEEE Transactions on Aerospace and Electronic Systems, 2001, 37(2): 643–654. doi: 10.1109/7.937475 |
[6] | Sun Y J, Liu Z P, Todorovis S, et al. Adaptive boosting for SAR automatic target recognition[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(1): 112–125. doi: 10.1109/TAES.2007.357120 |
[7] | Song S L, XU B, Yang J. SAR target recognition via supervised discriminative dictionary learning and sparse representation of the SAR-HOG feature[J]. Remote Sensing, 2016, 8(8): 683. |
[8] | Zhang H C, Nasrabadi N M, Zhang Y N, et al. Multi-view automatic target recognition using joint sparse representation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(3): 2481–2497. doi: 10.1109/TAES.2012.6237604 |
[9] | Dong G G, Kuang G Y, Wang N, et al. SAR target recognition via joint sparse representation of monogenic signal[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(7): 3316–3328. doi: 10.1109/JSTARS.2015.2436694 |
[10] | Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313 (5786): 504–507. doi: 10.1126/science.1127647 |
[11] | Ding J, Chen B, Liu H W, et al. Convolutional neural network with data augmentation for SAR target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(3): 364–368. |
[12] | Chen S Z, Wang H P, Xu F, et al. Target classification using the deep convolutional networks for SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8): 4806–4817. doi: 10.1109/TGRS.2016.2551720 |
[13] | Huang G, Huang G B, Song S J, et al. Trends in extreme learning machines: a review[J]. Neural Networks, 2015, 61: 32–48. doi: 10.1016/j.neunet.2014.10.001 |
[14] | Rokach L. Ensemble-based classifiers[J]. Artificial Intelligence Review, 2010, 33(1–2): 1–39. doi: 10.1007/s10462-009-9124-7 |
[15] | Goodfellow I, Bengio Y, Courville A. Deep Learning[M]. Cambridge, USA: MIT Press, 2016. |
[16] | Iamdola F N, Han S, Moskewica M W, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0. 5MB model size[EB/OL]. arXiv preprint: 1602.07360, 2016. |
[17] | Huang G B, Bai Z, Kasun L L C, et al. Local receptive fields based extreme learning machine[J]. IEEE Computational Intelligence Magazine, 2015, 10(2): 18–29. doi: 10.1109/MCI.2015.2405316 |
[18] | 李铁, 张新君.极限学习机在高光谱遥感图像分类中的应用[J].光电工程, 2016, 43(11): 62–68. doi: 10.3969/j.issn.1003-501X.2016.11.010 Li T, Zhang X J. Research of hyperspectral remote sensing image classification based on extreme learning machine[J]. Opto-Electronic Engineering, 2016, 43(11): 62–68. doi: 10.3969/j.issn.1003-501X.2016.11.010 |
[19] | Breiman L. Bagging predictors[J]. Machine Learning, 1996, 24(2): 123–140. |
[20] | 倪维平, 严卫东, 吴俊政, 等. MSTAR图像2D Gabor滤波增强与自适应阈值分割[J].光电工程, 2013, 40(3): 87–93. Ni W P, Yan W D, Wu J Z, et al. 2D gabor filter enhancing and adaptive thresholding for MSTAR image[J]. Opto-Electronic Engineering, 2013, 40(3): 87–93. |
[21] | Srinivas U, Monga V, Raj R G. SAR automatic target recognition using discriminative graphical models[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(1): 591–606. doi: 10.1109/TAES.2013.120340 |
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.
Flowchart of SAR target recognition algorithm based on random convolution features and ensemble extreme learning machines
SAR images and their corresponding visible images of partial types of targets in MSTAR database