NDLI: Image change detection based on cross-correlation coefficient by using Genetic Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Yunchen Pu Qiongcheng Xu Wei Wang
Copyright Year	2012
Description	Author affiliation: 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 (Yunchen Pu; Qiongcheng Xu; Wei Wang)
Abstract	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.
Starting Page	1
Ending Page	5
File Size	610661
Page Count	5
File Format	PDF
ISBN	9781467324953
e-ISBN	9781467324960
e-ISBN	9781467324946
DOI	10.1109/Agro-Geoinformatics.2012.6311693
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-02
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sociology Multi-objective optimizations Genetic algorithm Radiometry Robustness Cross-correlation Coefficient Detection Statistics Optimization Remote sensing Genetic algorithms
Content Type	Text
Resource Type	Article

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