By Topic

Remote sensing image classification based on dot density function weighted FCM clustering algorithm

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

7 Author(s)
Xiaofang Liu ; Univ. of Electron. Sci. & Technol. of China, Chengdu ; Xiaowen Li ; Ying Zhang ; Cunjian Yang
more authors

Based on the uncertainty and fuzziness of remote sensing images, a dot density function weighted fuzzy C-means (WFCM) clustering algorithm is proposed to carry out the fuzzy classification or the hard classification of remote sensing images. First, the algorithm considering data spatial distribution information and classification fuzziness is described. The fuzzy C-means algorithm is an unsupervised fuzzy classification method. Clustering precision of the algorithm is affected by its equal partition trend for data sets, which leads to the optimal solution of the algorithm may not be the correct partition in the data set of which cluster sample numbers are difference greatly. In order to overcome this drawback, a dot density function WFCM algorithm is proposed in this paper. The method has not only overcome the limitation of FCM to certain extent, but also been favorable convergence. Then the WFCM algorithm would be compared with the K-means algorithms by experiments in LANDSAT TM image. Finally classification result of the algorithms is analyzed systematically, and the experiment result shows the WFCM algorithm can improve classification accuracy for remote sensing images.

Published in:

Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International

Date of Conference:

23-28 July 2007