NDLI: A FCM algorithm for remote-sensing image classification considering spatial relationship and its parallel implementation

Content Provider	IEEE Xplore Digital Library
Author	Xue-Jing Gong Lin-Lin Ci Kang-Ze Yao
Copyright Year	2007
Description	Author affiliation: Beijing Inst. of Technol., Beijing (Xue-Jing Gong; Lin-Lin Ci)
Abstract	Fuzzy C-Means clustering is one of the most perfective and widely used algorithms based on objective function for unsupervised classification. Considering the spatial relationship of pixels when it is used in remote sensing imagery, Neighbor-based FCM algorithm is put forward with the method of modifying the value of fuzzy membership degrees with the neighbor information during the clustering iterations. We use dominant class, if it can be determined in a fixed neighbor region, or the weighted parameters based on the distance of neighbors to perfect the membership degrees of central pixel. Then parallel implement for the algorithm is also proposed by taking account into the communication complexity and the spatial relationship for image partition. In the end, the experimental data indicate the efficiency of the algorithm in decreasing the amount of clustering iterations and increasing the classified precision; the parallel algorithm also achieves the satisfied linear speedup.
Starting Page	994
Ending Page	998
File Size	860515
Page Count	5
File Format	PDF
ISBN	9781424410651
DOI	10.1109/ICWAPR.2007.4421576
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-02
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis fuzzy C-Means clustering neighbor region parallel algorithm Wavelet analysis Partitioning algorithms Pattern recognition Remote sensing Clustering algorithms Pattern analysis Pixel fuzzy membership degree Image classification
Content Type	Text
Resource Type	Article

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