DocumentCode
456910
Title
2D and 3D Vegetation Resource Parameters Assessment using Marked Point Processes
Author
Perrin, Guillaume ; Descombes, Xavier ; Zerubia, Josiane
Author_Institution
INRIA
Volume
1
fYear
0
fDate
0-0 0
Firstpage
1
Lastpage
4
Abstract
High resolution aerial and satellite images of forests have a key role to play in natural resource management. As they enable to study forests at the scale of trees, it is now possible to get a more accurate evaluation of the forest resources, from which can be deduced information on biodiversity and ecological sustainability. In that prospect, automatic algorithms are needed to give a further exploitation of the data and to assist human operators. In this paper, we present a stochastic geometry approach to extract 2D and 3D parameters of the trees, by modelling the stands as some realizations of a marked point process of ellipses or ellipsoids, whose points are the positions of the trees and marks their geometric features. This approach gives also the number of stems, their position, and their size. It is an energy minimization problem, where the energy embeds a regularization term (prior density), which introduces some interactions between the objects, and a data term, which links the objects to the features to be extracted. Results are shown on aerial images provided by the French National Forest Inventory (IFN)
Keywords
environmental science computing; feature extraction; forestry; stereo image processing; stochastic processes; vegetation; 2D images; 3D images; aerial images; biodiversity; ecological sustainability; ellipses; ellipsoids; energy minimization problem; feature extraction; forest resources; geometric features; marked point processes; natural resource management; satellite images; stochastic geometry; tree position; vegetation resource parameter assessment; Biodiversity; Biological system modeling; Data mining; Geometry; Humans; Image resolution; Resource management; Satellites; Stochastic processes; Vegetation mapping;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Conference_Location
Hong Kong
ISSN
1051-4651
Print_ISBN
0-7695-2521-0
Type
conf
DOI
10.1109/ICPR.2006.20
Filename
1698819
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