A method for managing evidential reasoning in a hierarchical hypothesis space
Artificial Intelligence
AI Expert
Information Processing and Management: an International Journal
Expert Systems: Design and Development
Expert Systems: Design and Development
Computers & Geosciences - Intelligent methods for processing geodata
Ontologies for geographic information processing
Computers & Geosciences - Intelligent methods for processing geodata
A national knowledge-based crop recognition in mediterranean environment
International Journal of Applied Earth Observation and Geoinformation
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
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Spatial, temporal and spectral complexity of remote sensing recognition tasks necessitates the use of Knowledge-Based Expert Systems (KBS). These systems are composed mainly of evidence and inference mechanisms: either domain-dependent inference (DDI) or domain-independent inference (DII). Selection of recognition strategies are typical of information foraging tasks and involve decisions regarding combinations of evidence and inference. This is highly dependent on the expected information gain (e.g. recognition accuracy and reliability) versus the cost/effort of constructing the evidential basis and the inference mechanism. This paper assessed a rule-based system (DDI) utilizing a sequent-oriented inference and a DII system utilizing the Dempster-Shafer evidential reasoning method. Quantification of evidence-inference-complexity-effort-accuracy relationships for a case study of land-use mapping on a wide regional scale allow a preliminary assessment of the relative performance of each strategy. Initial results indicate that a DII-based recognition system may function significantly better than a DDI-based system in large areas representing cases that had not been learnt during the evidence-extraction phase.