TY - JOUR

T1 - Automatic extraction and delineation of single trees from remote sensing data

AU - Wolf, Bernd Michael

AU - Heipke, Christian

N1 - Funding Information: The European Commission under the contract IST-1999-10510 funded parts of this work. The authors would like to thank the French company ISTAR, which provided the Grangemouth and the Paris data sets. Many thanks go to the Joanneum Research in Graz for the Hohentauern data set, and TopoSys GmbH, Germany, for making available the Ravensburg data set.

PY - 2007/10

Y1 - 2007/10

N2 - 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.

AB - 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.

KW - Automatic

KW - Real imagery

KW - Scale space

KW - Snakes

KW - Tree extraction

UR - http://www.scopus.com/inward/record.url?scp=34948899773&partnerID=8YFLogxK

U2 - 10.1007/s00138-006-0064-9

DO - 10.1007/s00138-006-0064-9

M3 - Article

AN - SCOPUS:34948899773

VL - 18

SP - 317

EP - 330

JO - Machine vision and applications

JF - Machine vision and applications

SN - 0932-8092

IS - 5

ER -