Remote sensing image recognition based on generalized regularized auto-encoders

doi:10.16511/j.cnki.qhdxxb.2018.25.006

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Abstract This paper addresses the problem of sample scarcity in SAR (synthetic aperture radar) images, which are very useful remote sensing sources but are very costly to obtain. SAR images have limited samples which makes it hard to get a standardized “good” model for even simple classification tasks. Then, the limited samples are expanded via generalization of the sample space using prior and a priori information including labels. Then, auto-learning models, regularized auto-encoders, are used to extract useful features from the generalized sample space. A regularization term is used to penalize generalized samples in an image recognition algorithm. The method produces good results on limit-sized subsets from the MNIST database and the SAR MSTAR image dataset. With limited input samples, the method gives better reconstruction errors and recognition accuracies in the SAR ship images than the state-of-art methods. Thus, this algorithm is shown to be successful in both theoretical analyses and tests.

Keywords generalized sample space generalized auto-encoders (GAE) synthetic aperture radar (SAR) limited samples unsupervised learning

ZTFLH:	TP753
	TP391.4

Issue Date: 15 February 2018

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	YANG Qianwen
	SUN Fuchun

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YANG Qianwen,SUN Fuchun. Remote sensing image recognition based on generalized regularized auto-encoders[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(2): 113-121.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2018.25.006 OR http://jst.tsinghuajournals.com/EN/Y2018/V58/I2/113

[1]	KAPLAN L M. Analysis of multiplicative speckle models for template-based SAR ATR[J]. IEEE Transactions on Aerospace and Electronic Systems, 2001, 37(4):1424-1432.
[2]	WANG J, SUN L J. Research on supervised manifold learning for SAR target classification[C]//IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA'09. Hong Kong, China:IEEE, 2009:140-142.
[3]	BERISHA V, SHAH N, WAAGEN D, et al. Sparse manifold learning with applications to SAR image classification[C]//IEEE International Conference Acoustics, Speech and Signal Processing. Honolulu, HI, USA:IEEE, 2007:1086-1089.
[4]	SCHERBAKOV A, HANSSEN R, VOSSELMAN G, et al. Ship wake detection using radon transforms of filtered SAR imagery[C]//Proceedings of the European Symposium on Satellite Remote Sensing:Microwave Sensing and Synthetic Aperture Radar. Taormina, Italy:International Society for Optics and Photonics, 1996:96-106.
[5]	种劲松, 朱敏慧. 高分辨率合成孔径雷达图像舰船检测方法[J]. 测试技术学报, 2003, 17(1):15-18.ZHONG J S, ZHU M H. Ship detection method of high-resolution SAR imagery[J]. J Test & Measurement Technology, 2003, 17(1):15-18. (in Chinese) url: http://dx.doi.org/t
[6]	CHAPELLE O, SCHLKOPF B, ZIEN A. Semi-supervised learning[J]. J Royal Stat Society, 2013, 49(2):215-239.
[7]	刘建伟, 刘媛, 罗雄麟. 半监督学习方法[J]. 计算机学报, 2015, 38(8):1592-1617.LIU J W, LIU Y, LUO X L. Semi-supervised learning methods[J]. Chinese J Computers, 2015, 38(8):1592-1617. (in Chinese)
[8]	KINGMA D P, REZENDE D J, MOHAMED S, et al. Semi-supervised learning with deep generative models[J]. Advances in Neural Information Processing Systems, 2014, 4:3581-3589.
[9]	SHINOZAKI T. Semi-supervised learning for convolutional neural networks using mild supervisory signals[C]//International Conference on Neural Information Processing. Kyoto, Japan:Springer International Publishing, 2016:381-388.
[10]	PAPANDREOU G, CHEN L C, MURPHY K P, et al. Weakly and semi-supervised learning of a deep convolutional network for semantic image segmentation[C]//IEEE International Conference on Computer Vision. Santiago, Chile:IEEE, 2015:1742-1750.
[11]	BLUM A. Semi-supervised learning[M]. New York:Springer, 2016:1936-1941.
[12]	GUO G D, DYER C R. Learning from examples in the small sample case:Face recognition[J]. IEEE Trans on Systems Man & Cybernetics, 2005, 35(3):477-488. url: http://dx.doi.org/Trans on Systems Man
[13]	YAN H B, LU J W, ZHOU X Z, et al. Multi-feature multi-manifold learning for single-sample face recognition[J]. Neurocomputing, 2014, 143(16):134-143.
[14]	LU J W, TAN Y P, WANG G. Discriminative multimanifold analysis for face recognition from a single training sample per person[C]//International Conference on Computer Vision. Barcelona, Spain:IEEE, 2011:1943-1950.
[15]	TAN X Y, CHEN S C, ZHOU Z H, et al. Face recognition from a single image per person:A survey[J]. Pattern Recognition, 2006, 39(9):1725-1745.
[16]	陈维桓. 微分几何初步[M]. 北京:北京大学出版社, 1990.CHEN W H. Differential geometry preliminary[M]. Beijing:Peking University Press, 1990. (in Chinese)
[17]	HUO X M, SMITH A K. A survey of manifold-based learning methods[C]//Recent Advances in Data Mining of Enterprise Data:Algorithms and Applications. Singapore:World Scientific Publishing Co. Inc., 2008:691-745.
[18]	BENGIO Y, COURVILLE A, VINCENT P. Representation learning:A review and new perspectives[J]. IEEE Trans on Pattern Analysis & Machine Intelligence, 2013, 35(8):1798-1828. url: http://dx.doi.org/Trans on Pattern Analysis
[19]	RIFAI S, VINCENT P, MULLER X, et al. Contractive auto-encoders:Explicit invariance during feature extraction[C]//International Conference on Machine Learning. Bellevue, WA, USA:Omnipress, 2011:833-840.
[20]	ALAIN G, BENGIO Y. What regularized auto-encoders learn from the data-generating distribution[J]. J Machine Learning Research, 2014, 15(1):3563-3593.
[21]	RIFAI S, MESNIL G, VINCENT P, et al. Higherorder contractive auto-encoder[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Berlin, Heidelberg, Germany:Springer, 2011:645-660.
[22]	YANG Q W, SUN F C. Small sample learning with high order contractive auto-encoders and application in SAR images[J]. arXiv preprint, 2017.
[23]	汪洪桥, 孙富春, 蔡艳宁, 等. 基于局部多分辨特征的SAR图像自动目标识别[J]. 清华大学学报(自然科学版), 2011, 51(8):1049-1054.WANG H Q, SUN F C, CAI Y N, et al. Automatic target recognition of SAR images based on local multi-resolution features[J]. J Tsinghua University (Science and Technology), 2011, 51(8):1049-1054.(in Chinese)

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