The use of expert system and supervised relaxation techniques to improve SPOT image classification using spatial context
Two methods have been tested in order to improve land use mapping in a post-classification refinement process: supervised relaxation and an expert system. Both methods use multiple sources of information and return satisfactory results. Statistical measurements of texture have been used to provide ancillary information for land use mapping at a super-class level (general classes) in a land cover classification tree. The reasoning model of the supervised relaxation technique is based upon the Bayesian theory. In contrast, the expert system uses the Dempster-Shafer reasoning scheme and allows evidence to be propagated at various levels in the land cover taxonomic hierarchy. The result of this approach may be a mixed-level map product, if the available amount of evidence is insufficient to decide among singleton competing labels. Thus, limitations in the entry-data set for accurate and fine classifications can be defined and resolved. The knowledge base of the expert system contains a set of 40 spatial context rules.
Bibliographic Reference: Paper presented: IGARSS '91, Espoo (FI), June 3-6, 1991
Availability: Available from (1) as Paper EN 36037 ORA
Record Number: 199110831 / Last updated on: 1994-12-02
Original language: en
Available languages: en