A region-based, graph-theoretic data model for the inference of second-order thematic information from remotely-sensed images

Abstract

Abstract A graph-theoretic data model, XRAG (eXtended Relational Attributed Graph), is described. The model and its associated data structure can be used to represent the structural properties (morphological and symbolic) of, and relations (spatial, topological, non-topological, quantitative and symbolic) between, discrete regions identified in a digital remotely-sensed image. The objective in developing this model is to allow second-order thematic information about the scene to be inferred from an analysis of these properties and relations. The paper briefly outlines the application of this model and an associated set of analytical techniques to infer land use from an initial land cover map derived from a digital remotely-sensed image.