A new modified fuzzy c-means algorithm for multispectral satellite images segmentation

Author

Thitimajshima, Punya

Author_Institution

Fac. of Eng., King Mongkut´´s Inst. of Technol., Bangkok, Thailand

Volume

4

fYear

2000

fDate

2000

Firstpage

1684

Abstract

The purpose of cluster analysis is to partition a data set into a number of disjoint groups or clusters. The members within a cluster are more similar to each other than members from different clusters. The fuzzy c-means (FCM) clustering is an iterative partitioning method that produces optimal c-partitions. Since the standard FCM algorithm takes a long time to partition a large data set. Because FCM program must read the entire data set into a memory for processing. This paper presents a method to speed up the FCM algorithm by reducing the number of numeric operations performed in each iteration, while keeping the exact result as the standard algorithm. The application of this method to multispectral satellite images has been evaluated, about 40% of time saving was obtained

Keywords

geophysical signal processing; geophysical techniques; image segmentation; multidimensional signal processing; remote sensing; terrain mapping; algorithm; cluster analysis; clustering; fuzzy c-means; fuzzy c-means algorithm; geophysical measurement technique; images segmentation; iterative partitioning method; land surface; multidimensional signal processing; multispectral remote sensing; multispectral satellite image; optimal c-partition; terrain mapping; Artificial satellites; Clustering algorithms; Convergence; Data engineering; Fuzzy sets; Image segmentation; Iterative algorithms; Iterative methods; Partitioning algorithms; Pixel;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-6359-0

Type

conf

DOI

10.1109/IGARSS.2000.857312

Filename

857312