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

关键词：

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.

引用

页码：1624 / 1626

页数：3

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