INTEGRATED ANALYSIS OF SPATIAL DATA FROM MULTIPLE SOURCES - USING EVIDENTIAL REASONING AND ARTIFICIAL NEURAL-NETWORK TECHNIQUES FOR GEOLOGICAL MAPPING

As the availability of digital spatial data, other than from remote se nsing, increases, it becomes increasingly important to develop algorit hms to handle both remote sensing and other spatial data. For classifi cation purposes, commonly used remote sensing algorithms such as the m aximum-likelihood classifier and the minimum-distance classifier can o nly be used to deal with spatial data of interval and ratio scales. Th ey are not applicable to spatial data of nominal or ordinal scale as e xemplified by data digitized from a categorical map. Bayesian theory, mathematical theory of evidence, and artificial neural networks, on th e other hand, are capable of handling data with any measurement scale. In this paper, we introduce an evidential reasoning and a back-propag ation feed-forward neural network algorithm and evaluate their applica tions to classification problems. A multisource data set including Lan dsat Thematic Mapper, aeromagnetic, radiometric, and gravity data has been used in the classification of four rock types in Melville Peninsu la, Northwest Territories, Canada. The highest overall accuracy of 96. 0 percent and average accuracy of 92.1 percent were achieved with the neural network algorithm while the evidential reasoning method produce d an overall accuracy of 94.7 percent and average accuracy of 89.3 per cent. The evidential reasoning method resulted in three highest indivi dual class accuracies out of the four classes.