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Proceedings Paper

Neighbor-based FCM clustering for remote sensing image and its parallel implementation
Author(s): Xuejing Gong; Kangze Yao
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Paper 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.

Paper Details

Date Published: 14 November 2007
PDF: 7 pages
Proc. SPIE 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 67891X (14 November 2007); doi: 10.1117/12.742837
Show Author Affiliations
Xuejing Gong, Beijing Institute of Technology (China)
The Academy of Equipment Command and Technology (China)
Kangze Yao, The Equipment Institute of PLA's Second Artillery (China)


Published in SPIE Proceedings Vol. 6789:
MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques

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