Olofsson, Kenneth
- Department of Forest Resource Management, Swedish University of Agricultural Sciences
Conference paper2005
Hagner Olle, Olofsson Kenneth, Olsson Håkan
The presentation reports on the work and preliminary results of an on-going 3-year project (2003-2005) aimed at the development of advanced methods for detection and measurement of single trees in high-resolution satellite imagery e.g. IKONOS, Quick Bird and airborne optical sensors. The apparent radiance pattern of single tree canopies and corresponding cast shadows depends on the viewing and illumination directions, tree species, topography and atmospheric conditions. There is also a large variation among trees of the same species, due to age, size, branch structure etc. The patterns are also affected by the spatial arrangement of neighbouring trees due to cast shadows and occlusion effects. If the forest is dense and regularly spaced the contrast between the sunlit part of tree crowns and shaded background allows for the use of simple “blob segmentation” algorithms or template matching techniques. However, sparse or open forest conditions motivate more advanced methods that explicitly accounts for the spatial arrangement of neighbouring trees. The detection algorithms developed in this project are based on a learning system approach using artificial neural networks with a feedback loop that incorporates an iteratively refined hypothesis of tree positions into the detection process. The networks are trained on datasets generated by a new geometric-optical model developed specifically for this application. The model is driven by detailed field plot information from the National Forest Inventory database. The presentation will focus on the specific image processing and detection algorithms to be used in the analysis
Single tree detection; geometric-optical model; high-resolution satellite images; neural networks; National forest inventory
Rapport / Skogsstyrelsen
2005, Volume: 2005:8c, number: 8C, pages: 92-95
Publisher: Skogsstyrelsens förlag
ForestSAT 2005, Operational Tools in Forestry Using Remote Sensing Techniques
Forest Science
https://res.slu.se/id/publ/6213