Image change detection using Gaussian mixture model and genetic algorithm

被引:29
作者
Celik, Turgay [1 ]
机构
[1] Natl Univ Singapore, A STAR, Comp Vis & Pattern Discovery Grp, Fac Sci,Dept Chem,Bioinformat Inst, Singapore 117548, Singapore
关键词
Gaussian mixture model; Genetic algorithm; Parameter estimation; Bayesian inference; Change detection; Difference image; Log-ratio image; Remote sensing; Optical image; Advanced synthetic aperture radar; UNSUPERVISED CHANGE DETECTION; SATELLITE IMAGES; EM ALGORITHM;
D O I
10.1016/j.jvcir.2010.09.005
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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. (c) 2010 Elsevier Inc. All rights reserved.
引用
收藏
页码:965 / 974
页数:10
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