Environmental Mapping Based on Spatial Variability and Computational Vision Models
dc.contributor.author | Kovalevskaya, Nelley | |
dc.contributor.author | Mitrophanova, Elena | |
dc.contributor.editor | Gnauck, Albrecht | |
dc.contributor.editor | Heinrich, Ralph | |
dc.date.accessioned | 2019-09-16T09:33:42Z | |
dc.date.available | 2019-09-16T09:33:42Z | |
dc.date.issued | 2003 | |
dc.description.abstract | Environmental maps show the probable environmental states of different types of land use or development of landscape in a geographic context. Remotely sensed data are particularly efficient for environmental mapping in order to outline major environmental types. Novel models of piecewise-homogeneous V images are used in environmental mapping to segment real images. The models consider both an image and a land cover map. Such a pair constitutes an example of a Markov random field specified by a joint Gibbs probability distribution of images and maps. Addition of spatial attributes v appears to be necessary in most areas where the differences in spatial data between regions in the image occur. | de |
dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/27113 | |
dc.publisher | Metropolis | |
dc.relation.ispartof | The Information Society and Enlargement of the European Union | |
dc.relation.ispartofseries | EnviroInfo | |
dc.title | Environmental Mapping Based on Spatial Variability and Computational Vision Models | de |
dc.type | Text/Conference Paper | |
gi.citation.publisherPlace | Marburg | |
gi.conference.date | 2003 | |
gi.conference.location | Cottbus | |
gi.conference.sessiontitle | Applications - Geographical Information Systems |