Spatial contextual classification and prediction models for mining geospatial data

TLDR

Spatial context modeling is crucial in geospatial classification, with Markov random fields and spatial autoregression commonly used, yet comparative studies between these approaches are scarce. The study argues that SAR imposes stricter assumptions on feature distributions and class boundaries than MRF and seeks to compare the two models. The authors compare SAR and MRF using both probabilistic analysis and experimental evaluation.

Abstract

Modeling spatial context (e.g., autocorrelation) is a key challenge in classification problems that arise in geospatial domains. Markov random fields (MRF) is a popular model for incorporating spatial context into image segmentation and land-use classification problems. The spatial autoregression (SAR) model, which is an extension of the classical regression model for incorporating spatial dependence, is popular for prediction and classification of spatial data in regional economics, natural resources, and ecological studies. There is little literature comparing these alternative approaches to facilitate the exchange of ideas. We argue that the SAR model makes more restrictive assumptions about the distribution of feature values and class boundaries than MRF. The relationship between SAR and MRF is analogous to the relationship between regression and Bayesian classifiers. This paper provides comparisons between the two models using a probabilistic and an experimental framework.