A comparison of hierarchical and partitional clustering techniques for multispectral image classification

被引：0
|
作者：
Wilson, HG [1 ]
Boots, B [1 ]
Millward, AA [1 ]
机构：
[1] Univ Waterloo, Dept Geog, Waterloo Lab Earth Obersvat, Waterloo, ON N2L 3G1, Canada
来源：
IGARSS 2002: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM AND 24TH CANADIAN SYMPOSIUM ON REMOTE SENSING, VOLS I-VI, PROCEEDINGS: REMOTE SENSING: INTEGRATING OUR VIEW OF THE PLANET | 2002年
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D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Unsupervised classification of remotely sensed data has traditionally been performed using partitional clustering procedures. This paper compares six classification results for a small Landsat 7 TM sub-image of Hainan Province in China. Of all clustering procedures, the hierarchical nearest neighbour linkage had the lowest classification accuracy, whereas the combinatorial K-means partitional procedure produced the best classification result.
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页码：1624 / 1626
页数：3
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