NDLI: An Improved FCM Algorithm Based on the SVDD for Unsupervised Hyperspectral Data Classification

Content Provider	IEEE Xplore Digital Library
Author	Niazmardi, S. Homayouni, S. Safari, A.
Copyright Year	2008
Abstract	Unsupervised classification approaches, also known as “clustering algorithms”, can be considered a solution to problems associated with the supervised classification of remotely sensed image data. The most important of these problems with respect to statistical classification algorithms is the lack of enough high quality training data and high dimensionality of hyperspectral data. In this paper, an improved clustering framework is developed and evaluated as a resolution to these problems. The proposed method enhances the Fuzzy C-Means (FCM) algorithm by using the Support Vector Domain Description (SVDD). The proposed algorithm operates in a similar manner as the FCM for the clustering and labeling of data vectors. However, for estimation of the cluster centers, the SVDD encircles the corresponding members and estimates the center of a containing sphere. By doing so, the effects of noise and outliers on the cluster centers are reduced, and more specifically, higher classification accuracy can be obtained. In spite of this advantage, there are two sets of parameters, namely, the SVDD's and FCM's parameters, both of which affect the performance of the proposed algorithm. Accordingly, the effects of these parameters and their optimum values have been evaluated as well. The evaluations of the results of experiments show that the proposed algorithm, due to the use of the SVDD algorithm, is more efficient than other clustering algorithms.
Starting Page	831
Ending Page	839
Page Count	9
File Size	1029215
File Format	PDF
ISSN	19391404
Volume Number	6
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Kernel Hyperspectral imaging Support vector machines Partitioning algorithms Equations SVDD Clustering FCM hyperspectral data kernel function
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Computers in Earth Sciences

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