Abstract
Abstract Spatial Informatics typically involves interpretation of remotely sensed images and analysis of multi-sources of data. Random perturbation in the observations and diversity of hypothesized models give rise to the uncertainty and difficulty of image interpretation and data analysis. The Minimum-Description-Length (MDL) principle is a beet established criterion, which selects the best model with the minimal length of jointly encoding the data and the model. Although in terms of probability, MDL criterion is equivalent to the Maximum Aposterior Probability (MAP) criterion, it is advantageous at Combining different data types and different model structures in a uniform measure – the total number of bits. It is more realistic to computerized information processing, because everything is discrete with limited resolution. This paper clarifies the formulation of the MDL criterion, its relationships to information theory, stochastic complexity, and Bayesian decision strategy. To sufficiently demonstrate t...
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