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

Author

Wilson, H.G. ; Boots, B. ; Millward, A.A.

Author_Institution

Dept. of Geogr., Waterloo Univ., Ont., Canada

Volume

3

fYear

2002

fDate

24-28 June 2002

Firstpage

1624

Abstract

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.

Keywords

hierarchical systems; image classification; remote sensing; statistical analysis; China; Hainan Province; Landsat 7 TM sub-image; combinatorial K-means partitional procedure; hierarchical clustering technique; hierarchical nearest neighbour linkage; multispectral image classification; partitional clustering technique; remotely sensed data; unsupervised classification; Clustering algorithms; Clustering methods; Data mining; Geography; Image analysis; Multispectral imaging; Partitioning algorithms; Pattern recognition; Remote sensing; Satellites;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2002. IGARSS '02. 2002 IEEE International

Print_ISBN

0-7803-7536-X

Type

conf

DOI

10.1109/IGARSS.2002.1026201

Filename

1026201