A Novel Fuzzy C-Means Method for Hyperspectral Image Classification

Author

Kuo, Bor-Chen ; Huang, Wen-chun ; Liu, Hsiang-chuan ; Tseng, Shiau-chian

Author_Institution

Grad. Sch. of Educ. Meas. & Stat., Nat. Taichung Univ., Taichung

Volume

2

fYear

2008

fDate

7-11 July 2008

Abstract

In this paper, a new fuzzy clustering, namely fuzzy c-weighted mean (FCWM), is being proposed. The cost function of the classical fuzzy c-mean (FCM) is defined by the distances from data to the cluster centers with their fuzzy memberships. Another idea for estimating the cluster centers originating form the idea of weighted mean applied in nonparametric weighted feature extraction (NWFE) is introduced to established a novel FCM-like clustering algorithm in this study. The real data experimental results show that the proposing FCWM outperforms the original FCM.

Keywords

feature extraction; fuzzy control; geophysical techniques; geophysics computing; image classification; FCM-like clustering algorithm; cluster centers estimation; data clustering; fuzzy C-means method; fuzzy C-weighted mean; fuzzy memberships; hyperspectral image classification; nonparametric weighted feature extraction; Hyperspectral imaging; Image classification; clustering; fuzzy c-mean (FCM); nonparametric weighted feature extraction (NWFE);

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International

Conference_Location

Boston, MA

Print_ISBN

978-1-4244-2807-6

Electronic_ISBN

978-1-4244-2808-3

Type

conf

DOI

10.1109/IGARSS.2008.4779166

Filename

4779166