Maximum Entropy and Bayesian Methods for Image and Spatial Analysis

Dear Colleagues,

The maximum entropy framework (Jaynes, 1957a) is a cornerstone of statistical inference, and it has a privileged position as the only consistent method for combining different data into a single image. It allows us to incorporate extra, prior knowledge about the object being imaged and leads to the selection of a probability density function that is consistent with our knowledge and introduces no unwarranted information. Any probability density function satisfying the constraints that have smaller entropy will contain more information and, hence, less uncertainty.

In a Bayesian view, probabilities are seen as degrees of belief that are modified by information, which is refined as more information becomes available. In the presence of limited information, Bayesian probabilities are often easily assigned where conventional probabilities cannot.

Due to these properties, both maximum entropy and Bayesian approaches have been used massively in image analysis and processing as well as in spatial statistics, i.e., analysis of data observed in geographical space. The combination of Bayesian approaches with the maximum entropy method provides a great inference method.

This Special Issue will accept unpublished original research papers and comprehensive reviews on maximum entropy and Bayesian methods with applications on image data as well as on more general spatial data.

Prof. Dr. Volker J Schmid
Prof. Dr. Zahra Amini Farsani
Guest Editors

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