By Topic

Fuzzy partitioning using a real-coded variable-length genetic algorithm for pixel classification

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

2 Author(s)
Maulik, U. ; Dept. of Comput. Sci., Kalyani Gov. Eng. Coll., India ; Bandyopadhyay, S.

The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. Real-coded variable string length genetic fuzzy clustering with automatic evolution of clusters is used for this purpose. The cluster centers are encoded in the chromosomes, and the Xie-Beni index is used as a measure of the validity of the corresponding partition. The effectiveness of the proposed technique is demonstrated for classifying different landcover regions in remote sensing imagery. Results are compared with those obtained using the well-known fuzzy C-means algorithm.

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:41 ,  Issue: 5 )