期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：1992
卷号：XXIX Part B3
页码：896-902
出版社：Copernicus Publications
摘要：Multisource data integration is reformulated as a problem of defining a problem space with goals and constraints.Solving the problem of detecting objects, estimating parameters of geometric and radiometric models andclassification of objects is described as searching for the goals by navigation through problem space which can bereduced to a tree search. An inference engine provides the mechanism for navigating through problem space. Bydefining the inference procedure as a backward chaining of rules it is possible to select only data which are relevantto current hypothesiS evaluation. Backward chaining also allows the handling of cases of missing data. Informationquality and error propagation are treated under the formalism of maximum likelihood f minimum cost decisionmaking. Likelihood vectors are stored or regenerated for future use.Arguments are given for not using the Dempster,Schaefer method. The approach of defining a search space and useinference engines for navigating from initial state to goal state is contrasted with the usual approach of data mergingby colour picture painting. The knowledge based approach is illustrated by hypothesis evaluation using both ordinal(remote sensing) data and nominal (GIS, attribute) data. Examples are provided of the integration of multispectraldata with radar data, and on model based image interpretation applied to the recognition of buildings in airphotos
关键词：multi source; data integration; inference; backward chaining; Bayes; remote sensing; geo information;system; knowledge engineering; knowledge based systems
