光电工程, 2016, 43 (4): 33, 网络出版: 2016-05-11  

半监督复合核图聚类在高光谱图像中的应用

Semi-supervised Graph Clustering with Composite Kernel and Its Application in Hyperspectral Image
作者单位
重庆大学光电技术与系统教育部重点实验室,重庆 400030
引用该论文

李志敏, 郝盼超, 黄鸿, 黄文. 半监督复合核图聚类在高光谱图像中的应用[J]. 光电工程, 2016, 43(4): 33.

LI Zhimin, HAO Panchao, HUANG Hong, HUANG Wen. Semi-supervised Graph Clustering with Composite Kernel and Its Application in Hyperspectral Image[J]. Opto-Electronic Engineering, 2016, 43(4): 33.

参考文献

[1] 李吉明,贾森,彭艳斌. 基于光谱特征和纹理特征协同学习的高光谱图像数据分类 [J].光电工程, 2012,39(11):88-94.

    LI Jiming,JIA Sen,PENG Yanbin. Hyperspectral data classification with spectral and texture features by co-training algorithm[J]. Opto-Electronic Engineering,2012,39(11):88-94.

[2] 赵春晖,齐滨. 基于模糊核加权 C-均值聚类的高光谱图像分类 [J].仪器仪表学报, 2012,33(9):2016-2021.

    ZHAO Chunhui,QI Bin. Hyperspectral image classification based on fuzzy kernel weighted c-means clustering [J]. Chinese Journal of Scientific Instrument,2012,33(9):2016-2021.

[3] 李巧兰. 基于约束的半监督聚类的图像分割算法研究 [D].西安:西安电子科技大学, 2014:1-2.

    LI Qiaolan. Semi-supervised clustering based on constraints for image segmentation [D]. Xi′an:Xidian University,2014:1-2.

[4] 冷明伟. 主动半监督聚类及其在社团检测中的应用研究 [D].兰州:兰州大学, 2014:14-23.

    LENG Mingwei. Research of active semi-supervised clustering and its application in community detection [D]. Lanzhou: Lanzhou University,2014:14-23.

[5] 陈小冬,尹学松,林焕祥. 基于判别分析的半监督聚类方法 [J].计算机工程与应用, 2010,46(6):139-143.

    CHEN Xiaodong,YIN Xuesong,LIN Huanxiang. Semi-supervised clustering approach with discriminant analysis [J]. Computer Engineering and Applications,2010,46(6):139-143.

[6] Camps-Valls Gustavo,Marsheva Tatyana V. Bandos,ZHOU Dengyong. Semi-supervised graph-based hyperspectral image classification [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892),2007,45(10):3044-3054.

[7] 李亚娥. 基于图的半监督分类算法研究 [D].西安:陕西师范大学, 2012:15-18. LI Ya′e. Research on graph-based semi-supervised classification algorithm [D]. Xi'an:Shanxi Normal University,2012:15-18.

[8] Papadopoulos Dimitris F,Simos Theodore E. A modified runge-kutta-nystr.m method by using phase lag properties for the numerical solution of orbital problems [J]. Applied Mathematics & Information Sciences(S1935-0090),2013,7(2):433-437.

[9] 谢娟英,郭文娟,谢维信,等. 基于样本空间分布密度的初始聚类中心优化 K-均值算法 [J].计算机应用研究, 2012, 29(3):888-892.

    XIE Juanying,GUO Wenjuan,XIE Weixin,et al. K-means clustering algorithm based on optimal initial centers related to pattern distribution of samples in space [J]. Applications Research of Computers,2012,29(3):888-892.

[10] 王峰,籍锦程,聂百胜. K-均值聚类模糊逻辑数据融合改进算法研究 [J].中北大学学报:自然科学版, 2014,35(6): 699-703.

    WANG Feng,JI Jincheng,NIE Baisheng. An improved fusion method of fuzzy logic based on k-mean clustering [J]. Journal of North University of China:Natural Science Edition,2014,35(6):699-703.

[11] 张慧哲,王坚. 基于初始聚类中心选取的改进 FCM聚类算法 [J].计算机科学, 2009,36(6):206-209.

    ZHANG Huizhe,WANG Jian. Improved fuzzy c-means clustering algorithm based on selecting initial clustering centers [J]. Computer Science,2009,36(6):206-209.

[12] Kannan S R,Ramathilagam S. Effective fuzzy c-means clustering algorithms for data clustering problems [J]. Expert Systems with Applications(S0957-4174),2012,39(7):6292–6300.

[13] ZHAO Weizhong,HE Qing,MA Huifang. Effective semi-supervised document clustering via active learning with instance-level constraints [J]. Knowledge & Information Systems(S0219-1377),2012,30(3):569-587.

[14] Ozer Sedat,CHEN Chi H. A set of new chebyshev kernel function for support vector machine pattern classification [J]. Pattern Recognition(S0031-3203),2011,44(7):1435-1447.

[15] ZHANG Rui,WANG Wenjian. Facilitating the application of support vector machine by using a new kernel [J]. Export Systems with Applications(S0957-4174),2011,38(11):14225-14230.

李志敏, 郝盼超, 黄鸿, 黄文. 半监督复合核图聚类在高光谱图像中的应用[J]. 光电工程, 2016, 43(4): 33. LI Zhimin, HAO Panchao, HUANG Hong, HUANG Wen. Semi-supervised Graph Clustering with Composite Kernel and Its Application in Hyperspectral Image[J]. Opto-Electronic Engineering, 2016, 43(4): 33.

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