On active contour models and balloons
CVGIP: Image Understanding
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Ein Arbeitsplatz zur halbautomatischen Luftbildanalyse
Mustererkennung 1986, 8. DAGM-Symposium
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
The automatic recognition of individual trees in aerial images of forests based on a synthetic tree crown image model
Remote Sensing and Image Interpretation
Remote Sensing and Image Interpretation
Tree detection from aerial imagery
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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In this paper, we present a novel approach for the automatic extraction of trees and the delineation of the tree crowns from remote sensing data, and report and evaluate the results obtained with different test data sets. The approach is scale-invariant and is based on co-registered colour-infrared aerial imagery and a digital surface model (DSM). Our primary assumption is that the coarse structure of the crown, if represented at the appropriate level in scale-space, can be approximated with the help of an ellipsoid. The fine structure of the crown is suppressed at this scale level and can be ignored. Our approach is based on a tree model with three geometric parameters (size, circularity and convexity of the tree crown) and one radiometric parameter for the tree vitality. The processing strategy comprises three steps. First, we segment a wide range of scale levels of a pre-processed version of the DSM. In the second step, we select the best hypothesis for a crown from the overlapping segments of all levels based on the tree model. The selection is achieved with the help of fuzzy functions for the tree model parameters. Finally, the crown boundary is refined using active contour models (snakes). The approach was tested with four data sets from different sensors and exhibiting different resolutions. The results are very promising and prove the feasibility of the new approach for automatic tree extraction from remote sensing data.