SAR Target Recognition with Feature Fusion Based on Stacked Autoencoder
Kang Miao① Ji Kefeng①* Leng Xiangguang① Xing Xiangwei② Zou Huanxin①
①(School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China) ②(Beijing Institute of Remote Sensing Information, Beijing 100192, China)
A feature fusion algorithm based on a Stacked AutoEncoder (SAE) for Synthetic Aperture Rader (SAR) imagery is proposed in this paper. Firstly, 25 baseline features and Three-Patch Local Binary Patterns (TPLBP) features are extracted. Then, the features are combined in series and fed into the SAE network, which is trained by a greedy layer-wise method. Finally, the softmax classifier is employed to fine tune the SAE network for better fusion performance. Additionally, the Gabor texture features of SAR images are extracted, and the fusion experiments between different features are carried out. The results show that the baseline features and TPLBP features have low redundancy and high complementarity, which makes the fused feature more discriminative. Compared with the SAR target recognition algorithm based on SAE or CNN (Convolutional Neural Network), the proposed method simplifies the network structure and increases the recognition accuracy and efficiency. 10-classes SAR targets based on an MSTAR dataset got a classification accuracy up to 95.88%, which verifies the effectiveness of the presented algorithm.
康 妙, 计科峰, 冷祥光, 邢相薇, 邹焕新. 基于栈式自编码器特征融合的SAR图像车辆目标识别[J]. 雷达学报, 2017, 6(2): 167-176.
Kang Miao, Ji Kefeng, Leng Xiangguang, Xing Xiangwei, Zou Huanxin. SAR Target Recognition with Feature Fusion Based on Stacked Autoencoder. JOURNAL OF RADARS, 2017, 6(2): 167-176.
Jiang Y, Chen J, and Wang R. Fusing local and global information for scene classification[J]. Optical Engineering, 2010, 49(4): 047001-047001-10.
[2]
Liu Z and Liu C. Fusion of color, local spatial and global frequency information for face recognition[J]. Pattern Recognition, 2010, 43(8): 2882-2890.
[3]
Mohamed R and Mohamed M. A Hybrid feature extraction for satellite image segmentation using statistical global and local feature[C]. Proceedings of the Mediterranean Conference on Information & Communication Technologies 2015. Springer International Publishing, 2016: 247-255.
[4]
Zou J, Li W, Chen C, et al.. Scene classification using local and global features with collaborative representation fusion[J]. Information Sciences, 2016, 348: 209-226.
[5]
王大伟, 陈定荣, 何亦征. 面向目标识别的多特征图像融合技术综述[J]. 航空电子技术, 2011, 42(2): 6-12. Wang Dawei, Chen Dingrong, and He Yizheng. A survey of feature-level image fusion based on target recognition[J]. Avionics Technology, 2011, 42(2): 6-12.
[6]
王璐, 张帆, 李伟, 等. 基于Gabor滤波器和局部纹理特征提取的SAR目标识别算法[J]. 雷达学报, 2015, 4(6): 658-665. Wang Lu, Zhang Fan, Li Wei, et al.. A method of SAR target recognition based on Gabor filter and local texture feature extraction[J]. Journal of Radars, 2015, 4(6): 658-665.
[7]
Lin C, Peng F, Wang B H, et al.. Research on PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm[J]. Journal of Electronic Science Technology, 2012, 10(4): 352-357.
[8]
Huan R, Liang R, and Pan Y. SAR target recognition with the fusion of LDA and ICA[C]. 2009 International Conference on Information Engineering and Computer Science, IEEE, Wuhan, China, 2009: 1-5.
[9]
Chaudhary M D and Upadhyay A B. Fusion of local and global features using stationary wavelet transform for efficient content based image retrieval[C]. 2014 IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS), IEEE, Bhopal, India, 2014: 1-6.
[10]
Hinton G E and Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.
[11]
Geng J, Fan J, Wang H, et al.. High-Resolution SAR image classification via deep convolutional autoencoders[J]. IEEE Geoscience & Remote Sensing Letters, 2015, 12(11): 1-5.
[12]
Chen Y, Lin Z, Zhao X, et al.. Deep learning-based classification of hyperspectral data[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2014, 7(6): 2094-2107.
[13]
Sun Z, Xue L, and Xu Y. Recognition of SAR target based on multilayer auto-encoder and SNN[J]. International Journal of Innovative Computing, Information and Control, 2013, 9(11): 4331-4341.
[14]
Chen Y W and Lin C J. Combining SVMs with Various Feature Selection[M]. In Feature Extraction: Foundations and Applications, Guyon I, Gunn S, Nikravesh M, and Zadeh L A Berlin, Germany: Springer, 2006: 315-324.
[15]
Chen Y W. Combining SVMs with various feature selection strategies[D]. [Master. dissertation], National Taiwan University, 2005.
[16]
El Darymli K, Mcguire P, Gill E W, et al.. Characterization and statistical modeling of phase in single-channel synthetic aperture radar imagery[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(3): 2071-2092.
[17]
Kapur J N, Sahoo P K, and Wong A K C. A new method for gray-level picture thresholding using the entropy of the histogram[J]. Computer Vision, Graphics, and Image Processing, 1985, 29(3): 273-285.
[18]
Mathworks. Morphology Fundamentals: Dilation and Erosion[OL]. http://tinyurl.com/q6zf.
[19]
Wolf L, Hassner T, and Taigman Y. Descriptor based methods in the wild[C]. Workshop on Faces in Real-Life Images: Detection, Alignment, and Recognition, 2008.
[20]
施彦, 韩力群, 廉小亲. 神经网络设计方法与实例分析[M]. 北京: 北京邮电大学出版社, 2009: 32-108. Shi Yan, Han Liqun, and Lian Xiao qin. Neural Network Design and Case Analysis[M]. Beijing: Beijing University of Posts and Telecommunications Press, 2009: 32-108.
[21]
Maaten L and Hinton G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9: 2579-2605.
[22]
Hu F, Zhang P, Yang R, et al.. SAR target recognition based on Gabor filter and sub-block statistical feature[C]. 2009 IET International Radar Conference, 2009: 1-4.
[23]
Song H, Ji K, Zhang Y, et al.. Sparse Representation-based SAR image target classification on the 10-class MSTAR data set[J]. Applied Sciences, 2016, 6(1): 26.
[24]
Morgan D A. Deep convolutional neural networks for ATR from SAR imagery[C]. SPIE Defense Security. International Society for Optics and Photonics, 2015: 94750F-94750F-13.
2017, Vol. 6 
