A New Weighted Fuzzy C-Means Clustering Algorithm for Remotely Sensed Image Classification

Fuzzy clustering model is an essential tool to find the proper cluster structure of given data sets in pattern and image classification. In this paper, a new weighted fuzzy C-Means (NW-FCM) algorithm is proposed to improve the performance of both FCM and FWCM models for high-dimensional multiclass p...

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Bibliographic Details
Published in:IEEE journal of selected topics in signal processing Vol. 5; no. 3; pp. 543 - 553
Main Authors: Chih-Cheng Hung, Kulkarni, S, Bor-Chen Kuo
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2011
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Algorithm design and analysis
Algorithms
Classification algorithms
Clustering
Clustering algorithms
Clusters
Discriminant analysis feature extraction (DAFE)
Feature extraction
Fuzzy
fuzzy C-means algorithm (FCM)
Fuzzy logic
Fuzzy set theory
fuzzy weighted C-means algorithm (FWCM)
nonparametric weighted feature extraction (NWFE)
Pattern recognition
Principal component analysis
Studies
ISSN:1932-4553, 1941-0484
Online Access:Get full text
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Summary:Fuzzy clustering model is an essential tool to find the proper cluster structure of given data sets in pattern and image classification. In this paper, a new weighted fuzzy C-Means (NW-FCM) algorithm is proposed to improve the performance of both FCM and FWCM models for high-dimensional multiclass pattern recognition problems. The methodology used in NW-FCM is the concept of weighted mean from the nonparametric weighted feature extraction (NWFE) and cluster mean from discriminant analysis feature extraction (DAFE). These two concepts are combined in NW-FCM for unsupervised clustering. The main features of NW-FCM, when compared to FCM, are the inclusion of the weighted mean to increase the accuracy, and, when compared to FWCM, the centroid of each cluster is included to increase the stability. The motivation of this work is to meliorate the well-known fuzzy C-Means algorithm (FCM) and a recently proposed fuzzy weighted C-Means algorithm (FWCM). Our finding is that the proposed algorithm gives greater classification accuracy and stability than that of FCM and FWCM. Experimental results on both synthetic and real data demonstrate that the proposed clustering algorithm will generate better clustering results than those of FCM and FWCM algorithms, in particularly for hyperspectral images.
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ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2010.2096797