Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jvcir.2010.09.005
Title: Image change detection using Gaussian mixture model and genetic algorithm
Authors: Celik, T. 
Keywords: Advanced synthetic aperture radar
Bayesian inference
Change detection
Difference image
Gaussian mixture model
Genetic algorithm
Log-ratio image
Optical image
Parameter estimation
Remote sensing
Issue Date: Nov-2010
Citation: Celik, T. (2010-11). Image change detection using Gaussian mixture model and genetic algorithm. Journal of Visual Communication and Image Representation 21 (8) : 965-974. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jvcir.2010.09.005
Abstract: In this paper, we propose a novel method for unsupervised change detection in multi-temporal satellite images of the same scene using Gaussian mixture model (GMM) and genetic algorithm (GA). The difference image data computed from multi-temporal satellite images of the same scene is modelled by using N components GMM. GA is used to estimate the parameters of the GMM. Then, the GMM of the difference image data is partitioned into two sets of distributions representing data distributions of "changed" and "unchanged" pixels by minimizing a cost function using GA. Bayesian inference is exploited together with the estimated data distributions of "changed" and "unchanged" pixels to achieve the final change detection result. The proposed method does not need any parameter tuning process, and is completely automatic. As a case study for the unsupervised change detection, multi-temporal advanced synthetic aperture radar (ASAR) images acquired by ESA Envisat on the recent flooding area in Bangladesh and parts of India brought on by two weeks of persistent rain and multi-temporal optical images acquired by Landsat 5 TM on a part of Alaska are considered. Change detection results are shown on real data and comparisons with the state-of-the-art techniques are provided. © 2010 Elsevier Inc. All rights reserved.
Source Title: Journal of Visual Communication and Image Representation
URI: http://scholarbank.nus.edu.sg/handle/10635/76340
ISSN: 10473203
DOI: 10.1016/j.jvcir.2010.09.005
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