By Topic

Image change detection based on cross-correlation coefficient by using Genetic 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
$33 $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)
Yunchen Pu ; Dept. of Automation, Shanghai Jiaotong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 800 Dong Chuan Road, Shanghai, 200240, China ; Qiongcheng Xu ; Wei Wang

In this paper, a novel unsupervised change detection approach based on cross-correlation coefficient is proposed. The cross-correlation coefficient is a measure of the similarity between two variables. The change detection problem can be understood as the process to partition two input images into two distinct regions, namely “changed” and “unchanged”, according to the binary change detection mask. Each region in the pair of the images of the corresponding position is considered as two sets of variables, whose cross-correlation coefficient is calculated in order to provide an optimal partition of the changed and unchanged regions. In the optimal partition, it is obvious that the cross-correlation coefficient of the set of the unchanged variables should be the maximum, while the absolute-value of that of the changed variables should be the minimum, because the corresponding unchanged regions are similar while the changed regions are quite different. Genetic Algorithm is used to obtain the optimal non-dominated solution as the change detection using cross-correlation coefficient is a multi-objective optimization problem. The simulation experiment shows that the result using the new method is effective and robust to radiometric difference.

Published in:

Agro-Geoinformatics (Agro-Geoinformatics), 2012 First International Conference on

Date of Conference:

2-4 Aug. 2012