By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)

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.

Published in:

Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on  (Volume:3 )

Date of Conference:

2-4 Nov. 2007