Abstract
A Gaussian mixture model (GMM) and Bayesian inferencing (BI) based unsupervised change detection method in satellite images is presented. The data distribution of the difference image, which is computed from satellite images of the same scene acquired at different time instances, is modelled by using GMM. The components of the overall GMM are separated into two classes to model the data distributions of changed and unchanged pixels. The weights of the components in each class are used to estimate the a priori probability of each corresponding class. The final change detection is achieved by applying BI to classify each pixel of the difference image into one or two classes.
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