Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.patrec.2011.05.008
Title: Bayesian change detection based on spatial sampling and Gaussian mixture model
Authors: Çelik, T. 
Keywords: Bayesian inferencing
Binary thresholding
Change detection
Difference image
Gaussian mixture model
Log-ratio image
Issue Date: 1-Sep-2011
Source: Çelik, T. (2011-09-01). Bayesian change detection based on spatial sampling and Gaussian mixture model. Pattern Recognition Letters 32 (12) : 1635-1642. ScholarBank@NUS Repository. https://doi.org/10.1016/j.patrec.2011.05.008
Abstract: A Gaussian mixture model (GMM) and Bayesian inferencing based unsupervised change detection algorithm is proposed to achieve change detection on the difference image computed from satellite images of the same scene acquired at different time instances. Each pixel of the difference image is represented by a feature vector constructed from the difference image values of the neighbouring pixels to consider the contextual information. The feature vectors of the difference image are modelled as a GMM. The conditional posterior probabilities of changed and unchanged pixel classes are automatically estimated by partitioning GMM into two distributions by minimizing an objective function. Bayesian inferencing is then employed to segment the difference image into changed and unchanged classes by using the conditional posterior probability of each class. Change detection results are shown on real datasets. © 2011 Elsevier B.V. All rights reserved.
Source Title: Pattern Recognition Letters
URI: http://scholarbank.nus.edu.sg/handle/10635/75646
ISSN: 01678655
DOI: 10.1016/j.patrec.2011.05.008
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