Automatic extraction and delineation of single trees from remote sensing data

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.

[1]  Joanne C. White,et al.  Comparison of airborne and satellite high spatial resolution data for the identification of individual trees with local maxima filtering , 2004 .

[2]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[3]  Sigfrid Schneider,et al.  Luftbild und Luftbildinterpretation , 1974 .

[4]  Åsa Persson,et al.  Detecting and measuring individual trees using an airborne laser scanner , 2002 .

[5]  Beat Koch,et al.  Development of Filtering , Segmentation and Modelling Modules for Lidar and Multispectral Data as a Fundament of an Automatic Forest Inventory System , 2004 .

[6]  L. Gabet,et al.  Construction automatique de modèle numérique de terrain à haute résolution en zone urbaine , 1994 .

[7]  Wolfgang Eckstein,et al.  Ein Arbeitsplatz zur halbautomatischen Luftbildanalyse , 1986, DAGM-Symposium.

[8]  Morten Larsen,et al.  Using ray-traced templates to find individual trees in aerial photographs , 1997 .

[9]  Mathias Schardt,et al.  ASSESSMENT OF FOREST PARAMETERS BY MEANS OF LASER SCANNING , 2002 .

[10]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[11]  S. Beucher,et al.  Watersheds of functions and picture segmentation , 1982, ICASSP.

[12]  Kim L. Boyer,et al.  Linearized vegetation indices based on a formal statistical framework , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Tony Lindeberg,et al.  Scale-Space Theory in Computer Vision , 1993, Lecture Notes in Computer Science.

[14]  Stephan Winter,et al.  Uncertain topological relations between imprecise regions , 2000, Int. J. Geogr. Inf. Sci..

[15]  Tomas Brandtberg,et al.  Automated delineation of individual tree crowns in high spatial resolution aerial images by multiple-scale analysis , 1998, Machine Vision and Applications.

[16]  Peng Gong,et al.  3D Model-Based Tree Measurement from High-Resolution Aerial Imagery , 2002 .

[17]  Bernd-Michael Wolf,et al.  Automatische Extraktion von Bäumen aus Fernerkundungsdaten , 2003 .

[18]  Laurent D. Cohen,et al.  On active contour models and balloons , 1991, CVGIP Image Underst..

[19]  Hannu Hyyppä,et al.  Automatic derivation of features related to forest stand attributes using laser scanner , 2000 .

[20]  Richard J. Pollock,et al.  Model-based approach to automatically locating tree crowns in high spatial resolution images , 1994, Remote Sensing.

[21]  B. Straub.  Automatic Extraction of Trees for 3 D-City Models from Images and Height Data , 2001 .

[22]  E. Baltsavias,et al.  Automatic Extraction of Man-Made Objects from Aerial and Space Images (II) , 1995 .

[23]  J. Koenderink.  The structure of images , 2004, Biological Cybernetics.

[24]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[25]  S. Reutebuch,et al.  BAYESIAN OBJECT RECOGNITION FOR THE ANALYSIS OF COMPLEX FOREST SCENES IN AIRBORNE LASER SCANNER DATA , 2002 .

[26]  F. Gougeon.  A Crown-Following Approach to the Automatic Delineation of Individual Tree Crowns in High Spatial Resolution Aerial Images , 1995 .

[27]  T. M. Lillesand,et al.  Remote sensing and image interpretation. Second edition , 1987 .

[28]  François A. Gougeon,et al.  Individual Tree Classification Using Meis-II Imagery , 1988, International Geoscience and Remote Sensing Symposium, 'Remote Sensing: Moving Toward the 21st Century'..

[29]  P. Litkey,et al.  Algorithms and methods of airborne laser-scanning for forest measurements , 2004 .

[30]  Robert J. Woodham,et al.  The automatic recognition of individual trees in aerial images of forests based on a synthetic tree crown image model , 1996 .