Prototype Theory Based Feature Representation for PolSAR Images
Huang Xiaojing① Yang Xiangli① Huang Pingping② Yang Wen*①
①(School of Electronic Information, Wuhan University, Wuhan 430072, China) ②(Radar Research Institute, Inner Mongolia University of Technology, Hohhot 010051, China)
This study presents a new feature representation approach for Polarimetric Synthetic Aperture Radar (PolSAR) image based on prototype theory. First, multiple prototype sets are generated using prototype theory. Then, regularized logistic regression is used to predict similarities between a test sample and each prototype set. Finally, the PolSAR image feature representation is obtained by ensemble projection. Experimental results of an unsupervised classification of PolSAR images show that our method can efficiently represent polarimetric signatures of different land covers and yield satisfactory classification results.
Cloude S. Polarisation: Applications in Remote Sensing[M]. London, U.K.: Oxford University Press, 2009. 周晓光, 匡纲要, 万建伟. 极化SAR图像分类综述[J]. 信号处理, 2008, 24(5): 806-812.
[2]
Zhou Xiao-guang, Kuang Gang-yao, and Wan Jian-wei. A review of polarimetric SAR image classification[J]. Signal Processing, 2008, 24(5): 806-812.
[3]
Cloude S R and Pottier E. A review of target decomposition theorems in radar polarimetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(2): 498-518.
[4]
Freeman A and Durden S L. A three-component scattering model for polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 963-973.
[5]
Yamaguchi Y, Moriyama T, Ishido M, et al.. Four-component scattering model for polarimetric SAR image decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(8): 1699-1706. 闫剑, 李洋, 尹嫱, 等. 引入有取向二面角散射的Freeman-Durden分解[J]. 雷达学报, 2014, 3(5): 574-582.
[6]
Yan Jian, Li Yang, Yin Qiang, et al.. Freeman-Durden decomposition with oriented dihedral scattering[J]. Journal of Radars, 2014, 3(5): 574-582. 滑文强, 王爽, 侯彪. 基于半监督学习的SVM-Wishart极化 SAR 图像分类方法[J]. 雷达学报, 2015, 4(1): 93-98.
[7]
Hua Wen-qiang, Wang Shuang, and Hou Biao. Semi-supervised learning for classification of polarimetric SAR images based on SVM-Wishart[J]. Journal of Radars, 2015, 4(1): 93-98. 李洪忠, 程劲松, 王超, 等. 基于相似性的POLSAR 占优散射归类及非监督聚类[J]. 电子与信息学报, 2012, 34(6): 1501-1505.
[8]
Li Hong-zhong, Cheng Jin-song, Wang Chao, et al.. POLSAR dominant scattering mechanism clustering and unsupervised classification based on similarity[J]. Journal of Electronics & Information Technology, 2012, 34(6): 1501-1505. 陈博, 王爽, 焦李成, 等. 利用0-1矩阵分解集成的极化SAR图像分类[J]. 电子与信息学报, 2015, 37(6): 1495-1501.
[9]
Chen Bo, Wang Shuang, Jiao Li-cheng, et al.. Polarimetric SAR image classification via weighted ensemble based on 0-1 matrix decomposition[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1495-1501.
[10]
Wohlhart P, Kostinger M, Donoser M, et al.. Optimizing 1-Nearest prototype classifiers[C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013: 460-467.
[11]
Quattoni A, Collins M, and Darrell T. Transfer learning for image classification with sparse prototype representations[C].IEEE Conference on Computer Vision and Pattern Recognition, 2008: 1-8.
[12]
王宇, 杨莉. 模糊k-prototypes聚类算法的一种改进算法[J]. 大连理工大学学报, 2003, 43(6): 849-852. Wang Yu and Yang Li. An improved algorithm for fuzzy k-prototypes algorithm[J]. Journal of Dalian University of Technology, 2003, 43(6): 849-852. 陈韡, 王雷, 蒋子云. 基于K-prototypes的混合属性数据聚类算法[J]. 计算机应用, 2010, 30(8): 2003-2005.
[13]
Chen Wei, Wang Lei, and Jiang Zi-yun. K-prototypes based clustering algorithm for data mixed with numeric and categorical values[J]. Journal of Computer Application, 2010, 30(8): 2003-2005.
[14]
Crammer K, Gilad-Bachrach R, Navot A, et al.. Margin analysis of the LVQ algorithm[C]. Advances in Neural Information Processing Systems, 2002: 462-469.
[15]
Kostinger M, Wohlhart P, Roth P M, et al.. Joint learning of discriminative prototypes and large margin nearest neighbor classifiers[C]. IEEE International Conference on Computer Vision, 2013: 3112-3119.
[16]
Dai D and Gool L V. Ensemble projection for semi-supervised image classification[C]. IEEE International Conference on Computer Vision, 2013: 2072-2079.
[17]
Wang D. Online object tracking with sparse prototypes[J]. IEEE Transactions on Image Processing, 2013, 22(1): 314-325.
[18]
Lin Z, Jiang Z, and Davis L S. Recognizing actions by shape-motion prototype trees[C]. 2009 IEEE 12th International Conference on Computer Vision, 2009: 444-451.
[19]
Molinier M, Laaksonen J, Rauste Y, et al.. Detecting changes in polarimetric SAR data with content-based image retrieval[C]. IEEE International on Geoscience and Remote Sensing Symposium, 2007: 2390-2393.
[20]
Lee J S and Pottier E. Polarimetric Radar Imaging: From Basics to Applications[M]. Boca Raton, FL: CRC Press, 2009.
[21]
Achanta R, Shaji A, Smith K, et al.. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274-2282.
[22]
Kersten P R, Lee J S, and Ainsworth T L. Unsupervised classification of polarimetric synthetic aperture radar images using fuzzy clustering and EM clustering[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(3): 519-527.
[23]
Collins M, Schapire R E, and Singer Y. Logistic regression, adaboost and bregman distances[J]. Machine Learning, 2002, 48(1): 253-285.
[24]
Lee J S, Grunes M R, Pottier E, et al.. Unsupervised terrain classification preserving polarimetric scattering characteristics[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(4): 722-731.
[25]
Zhao Y and Karypis G. Criterion functions for document clustering: experiments and analysis[R]. Technical Report, 2001, 1: 40.
2016, Vol. 5 
