Title :
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
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);
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
DOI :
10.1109/IGARSS.2008.4779166