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《Engineering of Surveying and Mapping》 2009-01
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The comparative research of initializing cluster centers for unsupervised classification

WEI Cong-ling1,FU Li-ping2 (1.College of Geography Science,Southwest University,Chongqing 400715,China;2.Dept.of Information Engineering,Henan Economic Management School,Nanyang 473000,China)  
The initializing cluster centers have important influence on the classification process and result of the remote sensing image for unsupervised classification.A good initializing cluster center can improve the efficiency and precision for the classification.In this paper,as estimation standards,the distance between classes and the standard deviation in class were selected,and according to them,some initializing cluster centers were compared.The results showed that the most-least distance method has higher precision and lower efficiency,and the means-standard deviation method has lower precision and higher efficiency.
【CateGory Index】: TP751
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1 ZHANG Wen-jun~(1,2,3),GU Xing-fa~(1,2,3),CHEN Liang-fu~(2,3),YU Tao~(2,3),XU Hua~(2,3)(1.School of Automation Engineering,the University of Electronic Science and Technology of China,Sichuan Chengdu 610054,China;2.State Key Laboratory of Remote Sensing Science,Jointly Sponsored by the Institute of Remote Sensing Applications ofChinese Academy of Sciences and Beijing Normal University,Beijing 100101,China;3.The Center for National Spaceborne Demonstration,Beijing 100101,China);An Algorithm for Initilizing of K-Means Clustering Based on Mean-standard Deviation[J];Journal of Remote Sensing;2006-05
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2 WANG Huilian~ ① , WU Fang~ ② ,WANG Baoshan~ ② , QIAN Haizhong~ ① (①Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450052; ②Henan Polytechnic University, Jiaozuo 454000);Improved algorithm for extracting skeleton line of polygon based on mathematical morphology[J];Science of Surveying and Mapping;2006-01
3 LIN Hui, SHU Ning, DU Pei jun;Application of Mathematical Morphology in Edge Detection of Remote Sensing Images[J];Bulletin of Surveying and Mapping;2003-12
4 WANG Fang,XIA Li-hua(School of Geographical Sciences,Guangzhou University, Guangzhou 510405,China);Research on Application of Mathematical Morphology in Edge Detection of High Resolution Remote Sensing Image[J];Geomatics & Spatial Information Technology;2006-02
5 MING Dongping, LUO Jiancheng, ZHOU Chenghu, WANG Jing (The State Key Lab of Resources and Environment Information System, IGSNRR, CAS, Beijing 100101, China; School of Computer, Northwestern Polytechnical University, Xi'an 710072, China);Research on High Resolution Remote Sensing Image Segmentation Methods Based on Features and Evaluation of Algorithms[J];Geo-Information Science;2006-01
6 LI Xiao-Feng1,ZHANG Shu-Qing1,LIU Qiang2,ZHANG Bai1,LIU Dian-Wei1,LU Bi-Bo3,NA Xiao-Dong1(1.Northeast Institute of Geography and Agroecology,CAS,Changchun 130012,China;2.College of Mathematics and Computation Science,Shenzhen University,Shenzhen 518060,China;3.College of Computer Science & Technology,Henan Polytechnic University,Jiaozuo 454000,China);FAST SEGMENTATION METHOD OF HIGH-RESOLUTION REMOTE SENSING IMAGE[J];Journal of Infrared and Millimeter Waves;2009-02
7 BAO Jian,LI Xiao-run(College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China);Unsupervised classification of remote images using K-mean algorithm[J];Mechanical & Electrical Engineering Magazine;2008-03
8 Xie Qianli1 Cheng Chengqi1 Ma Ting2 1(Institute of Remote Sensing,Peking University,Beijing 100871) 2(State Key Lab. of Resources and Environment Information System,Institute of Geography Science & Natural Resources Research,CAS,Beijing 100101);A New Approach of Extracting Road from High-Resolution Remote Sensing Images[J];Computer Engineering and Applications;2006-17
9 LAI Yu-xia,LIU Jian-ping College of Electronic Information,Zhejiang Sci-Tech University,Hangzhou 310018,China;Optimization study on initial center of K-means algorithm[J];Computer Engineering and Applications;2008-10
10 LI Su-mei,HAN Guo-qiang;Method of image region segmentation based on K-means clustering algorithm[J];Computer Engineering and Applications;2008-16
【Secondary Citations】
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1 Wu Jinglan 1 Zhu Wenxing 21 (Department of Computer Science,Minjiang College,Fuzhou350002) 2 (Department of Computer Science and Technology,Fuzhou University,Fuzhou350002);An Iterated Local Search Algorithm for K-Means Clustering[J];Computer Engineering and Applications;2004-22
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