Citation: | Huang Xiaojing, Yang Xiangli, Huang Pingping, Yang Wen. Prototype Theory Based Feature Representation for PolSAR Images[J]. Journal of Radars, 2016, 5(2): 208-216. doi: 10.12000/JR15071 |
[1] |
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.
|
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