The integration of spatial context information in an experimental knowledge-based system and the supervised relaxation algorithm—two successful approaches to improving SPOT-XS classification

Abstract

This paper describes two different methods which integrate contextual information in a classification process. This process aims to refine the map products given by the application of a common parametric classification algorithm. The first method is the well known Supervised Relaxation Algorithm, and makes use of the first classification, with additional contextual information. The contextual information is derived either from texture features or from other map products introducing additional information on the existing land use classes. The second method is a knowledge-based system, which makes use of image and geographical context rules. The probability figures, derived from the image classifier and the rule base are combined by the use of the Dempster-Shafer reasoning scheme. Experiments using satellite data from the Loir et Cher region (Central France), together with the appropriate ground truth data, have shown that both methods return improved classification products in terms of thematic and statistical accuracy, compared to using a parametric: image classifier alone.