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：2002
卷号：XXXIV-3/W4
出版社：Copernicus Publications
摘要：A bottleneck in the use of Geographic Information Systems (GIS) is the cost of data acquisition. In our case, we are in- terested in producing GIS layers containing useful information for river .ood impact assessment. Geometric models can be used to describe regions of the data which correspond to man-made constructions. Probabilistic models can be used to describe vegetation and other features. Our purpose is to compare geometric and probabilistic models on small regions of interest in lidar data, in order to choose which type of models renders a better description in each re- gion. To do so, we use the Minimum Description Length prin- ciple of statistical inference, which states that best descrip- tions are those which better compress the data. By comparing computer programs that generate the data under di.erent as- sumptions, we can decide which type of models conveys more useful information about each region of interest
