[1] El-Darymli K, McGuire P, Power D, et al. Target detection in synthetic aperture radar imagery: A state-of-the-art survey[J].Journal of Applied Remote Sensing, 2013, 7(1): 071598. doi:  10.1117/1.JRS.7.071598
[2] Steenson B O. Detection performance of a mean-level threshold[J].IEEE Transactions on Aerospace and Electronic Systems, 1968, 4(4): 529–534.
[3] Gandhi P P and Kassam S A. Analysis of CFAR processors in homogeneous background[J].IEEE Transactions on Aerospace and Electronic Systems, 1988, 24(4): 427–445. doi:  10.1109/7.7185
[4] Novak L M and Hesse S R. On the performance of order-statistics CFAR detectors[C]. IEEE 25th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, USA, Nov. 1991: 835–840.
[5] Smith M E and Varshney P K. VI-CFAR: A novel CFAR algorithm based on data variability[C]. IEEE National Radar Conference, Syracuse, NY, May 1997: 263–268.
[6] Ai Jia-qiu, Qi Xiang-yang, Yu Wei-dong, et al. A new CFAR ship detection algorithm based on 2-D joint log-normal distribution in SAR images[J].IEEE Geoscience and Remote Sensing Letters, 2010, 7(4): 806–810. doi:  10.1109/LGRS.2010.2048697
[7] di Bisceglie M and Galdi C. CFAR detection of extended objects in high-resolution SAR images[J].IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4): 833–843. doi:  10.1109/TGRS.2004.843190
[8] Jung C H, Kwag Y K, and Song W Y. CFAR detection algorithm for ground target in heterogeneous clutter using high resolution SAR image[C]. 3rd International Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Seoul, South Korea, Sep. 2011: 1–4.
[9] Olson C F and Huttenlocher D P. Automatic target recognition by matching oriented edge pixels[J].IEEE Transactions on Image Processing, 1997, 6(1): 103–113. doi:  10.1109/83.552100
[10] Kaplan L M. Improved SAR target detection via extended fractal features[J].IEEE Transactions on Aerospace and Electronic Systems, 2001, 37(2): 436–451. doi:  10.1109/7.937460
[11] Sandirasegaram N M. Spot SAR ATR using wavelet features and neural network classifier[R]. Defence Research and Development Canada Ottawa (Ontario), 2005.
[12] Tan Yi-hua, Li Qing-yun, Li Yan-sheng, et al. Aircraft detection in high-resolution SAR images based on a gradient textural saliency map[J].Sensors, 2015, 15(9): 23071–23094. doi:  10.3390/s150923071
[13] Jao J K, Lee C E, and Ayasli S. Coherent spatial filtering for SAR detection of stationary targets[J].IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(2): 614–626. doi:  10.1109/7.766942
[14] Zhao Q and 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
[15] Sun Yi-jun, Liu Zhi-peng, Todorovic 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
[16] Keydel E R, Lee S W, and Moore J T. MSTAR extended operating conditions: A tutorial[C]. Proc. SPIE 2757, Algorithms for Synthetic Aperture Radar Imagery III, Orlando, FL, Apr. 1996: 228–242.
[17] Krizhevsky A, Sutskever I, and Hinton G E. ImageNet classification with deep convolutional neural networks[C]. International Conference on Neural Information Processing Systems, South Lake Tahoe, Nevada, USA, Dec. 2012: 1097–1105.
[18] Ding Jun, Chen Bo, Liu Hong-wei, et al. Convolutional neural network with data augmentation for SAR target recognition[J].IEEE Geoscience and Remote Sensing Letters, 2016, 13(3): 364–368.
[19] Chen Si-zhe, Wang Hai-peng, Xu Feng, 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
[20] Diao Wen-hui, Sun Xian, Zheng Xin-wei, et al. Efficient saliency-based object detection in remote sensing images using deep belief networks[J].IEEE Geoscience and Remote Sensing Letters, 2016, 13(2): 137–141. doi:  10.1109/LGRS.2015.2498644
[21] Chen Jie-hong, Zhang Bo, and Wang Chao. Backscattering feature analysis and recognition of civilian aircraft in TerraSAR-X images[J].IEEE Geoscience and Remote Sensing Letters, 2015, 12(4): 796–800. doi:  10.1109/LGRS.2014.2362845
[22] Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: A simple way to prevent neural networks from overfitting[J].The Journal of Machine Learning Research, 2014, 15(1): 1929–1958.
[23] Glorot X, Bordes A, and Bengio Y. Deep sparse rectifier neural networks[C]. 14th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA, 2011: 315–323.
[24] Qian Ning. On the momentum term in gradient descent learning algorithms[J].Neural Networks, 1999, 12(1): 145–151. doi:  10.1016/S0893-6080(98)00116-6