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Inference versus evidence in reasoning remote sensing recognition, with an information foraging perspective

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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.
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Document Type: Research Article

Affiliations: 1: Department of Transportation and Geoinformation Engineering, Faculty of Civil & Environmental Engineering, Technion - Israel Institute of Technology, Haifa 32000, Israel 2: Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Center, Bet Dagan 50250, Israel

Publication date: January 1, 2007

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