An iterative spectral-spatial Bayesian labeling approach for unsupervised robust change detection on remotely sensed multispectral imagery

  • Rafael Wiemker
Low Level Processing II
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1296)


In multispectral remote sensing, change detection is a central task for all kinds of monitoring purposes. We suggest a novel approach where the problem is formulated as a Bayesian labeling problem. Considering two registered images of the same scene but different recording time, a Bayesian probability for ‘Change’ and ‘NoChange’ is determined for each pixel from spectral as well as spatial features. All necessary parameters are estimated from the image data itself during an iterative clustering process which updates the current probabilities.

The contextual spatial features are derived from Markov random field modeling. We define a potential as a function of the probabilities of neighboring pixels to belong to the same class.

The algorithm is robust against spurious change detection due to changing recording conditions and slightly misregistered high texture areas. It yields successful results on simulated and real multispectral multitemporal aerial imagery.


Change Detection Spectral Band Posteriori Probability Conditional Probability Density Markov Random Field Modeling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Rafael Wiemker
    • 1
  1. 1.II. Institut für ExperimentalphysikUniversität HamburgHamburgGermany

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