Active contour and hill climbing for tree crown detection and delineation

Photogrammetric Engineering and Remote Sensing

Volumn 76, Issue 10, 2010, Pages 1169-1181

  Ke, Yinghai ^a,b   Zhang, Wenhua ^b   Quackenbush, Lindi J ^b  

^a PACIFIC NORTHWEST NATIONAL LABORATORY   (United States)

^b STATE UNIVERSITY OF NEW YORK   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE SEGMENTATION;

HIGH SPATIAL RESOLUTION IMAGERY; HILL CLIMBING ALGORITHMS; INDIVIDUAL TREE CROWN; NORWAY SPRUCE STANDS; PANCHROMATIC IMAGES; REGION BASED ACTIVE CONTOURS; REGION GROWING ALGORITHM; SHAPE CHARACTERISTICS;

FORESTRY;

ACCURACY ASSESSMENT; ALGORITHM; CANOPY ARCHITECTURE; CLUSTER ANALYSIS; COLOR; COMMUNITY COMPOSITION; CONIFEROUS FOREST; CONTOUR MAP; DIAMETER; DIGITAL IMAGE; FOREST ECOSYSTEM; FOREST HEALTH; IMAGING METHOD; PANCHROMATIC IMAGE; PIXEL; QUICKBIRD; SPATIAL RESOLUTION; SPECTRAL ANALYSIS;

ALGORITHMS; EXTRACTIVES; FORESTRY; IMAGE ANALYSIS; MOUNTAINS; PLANTS; SEGMENTATION;

PICEA ABIES;

EID: 80052483375     PISSN: 00991112     EISSN: None     Source Type: Journal    
DOI: 10.14358/PERS.76.10.1169     Document Type: Review

References (29)

1. Brandtberg, T., 1998. Automated delineation of individual tree crowns in high spatial resolution aerial images by multiple-scale analysis, Machine Vision and Applications, 11:64-73. (Pubitemid 128421856)
2. Bunting, P., and R.M. Lucas, 2006. The delineation of tree crowns in Australian mixed species forests using hyperspectral Compact Airborne Spectrographic Imager (CASI) data, Remote Sensing of Environment, 101:230-248. (Pubitemid 43343904)
3. Culvenor, D.S., 2000. Development of a Tree Delineation Algorithm for Application to High Spatial Resolution Digital Imagery of Australian Native Forest, Ph.D. dissertation, University of Melbourne, Melbourne, Australia.
4. Culvenor, D.S., 2002. TIDA: An algorithm for the delineation of tree crowns in high spatial resolution remotely sensed imagery, Computers & Geosciences, 28(1):33-44. (Pubitemid 34102694)
5. Erikson, M., 2004. Species classification of individually segmented tree crowns in high-resolution aerial images using radiometric and morphologic image measures, Remote Sensing of Environment, 91(3):469-477. (Pubitemid 38791560)
6. Gong, P., Y. Sheng, and G.S. Biging, 2002. 3D model-based tree measurement from high-resolution aerial imagery, Photogrammetric Engineering & Remote Sensing, 68(11):1203-1212. (Pubitemid 35235310)
7. Gonzalez, R.C., and R.E. Woods, 2008. Digital Image Processing, Third edition, Prentice Hall, Upper Saddle River, New Jersey, 954 p.
8. Gougeon, F.A., 1995. A crown-following approach to the automatic delineation of individual tree crowns in high-spatial resolution aerial images, Canadian Journal Remote Sensing, 21(3):274-284.
9. Gougeon, F.A., and D.G. Leckie, 2003. Forest information extraction from high spatial resolution images using an individual tree crown approach, Information Report BC-X-396, Natural Resources Canada, Canadian Forest Service (F.A. Gougeon and D.G. Leckie, editors), Victoria, B.C., Pacific Forestry Centre, pp. 1-17.
10. Huang, S.B., and R. Shibasaki, 1995. Development of genetic algorithm hill-climbing method for spatio-temporal interpolation, Proceedings of The 6th symposium on Functional Image Information System IIS, April, Tokyo, Japan, pp. 81-86.
11. Kangas, A., and M. Maltamo, 2006. Forest Inventory: Methodology and Applications (Managing Forest Ecosystem), Springer, The Netherlands, 362 p.
12. Kass, M., A. Witkin, and D. Terzopoulos, 1987. Snakes: Active contour models, International Journal of Computer Vision, 1(4):321-331.
13. Ke, Y., and L.J. Quackenbush, 2010. A comparison of three methods for automatic tree crown detection and delineation methods from high spatial resolution imagery, International Journal of Remote Sensing, in press.
14. Larmar, W.R., J.B. McGraw, and T.A. Warner, 2005. Multitemporal censusing of a population of eastern hemlock (Tsuga Canadensis L.) from remotely sensed imagery using an automated segmentation and reconciliation procedure, Remote Sensing of Environment, 94(1):133-143. (Pubitemid 39527204)
15. Leckie, D.G., F.A. Gougeon, N. Walsworth, and D. Paradine, 2003. Stand delineation and composition estimation using semiautomated individual tree crown analysis, Remote Sensing of Environment, 85(3):355-369. (Pubitemid 36533261)
16. Leckie, D.G., C. Jay, F.A. Gougeon, R. Sturrock, and D. Paradine, 2004. Detection and assessments of trees with Phellinus weirii (Laminated root rot) using high resolution multispectral imagery, International Journal of Remote Sensing, 25(4):793-818. (Pubitemid 38329664)
17. Leckie, D.G., F.A. Gougeon, S. Tinis, T. Nelson, C.N. Burnet, and D. Paradine, 2005. Automated tree recognition in old growth conifer stands with high resolution digital Imagery, Remote Sensing of Environment, 94(3):311-326. (Pubitemid 40118063)
18. Li, C., C.Y. Kao, J.C. Gore, and Z. Ding, 2007. Implicit active contours driven by local binary fitting energy, Proceedings of 2007 IEEE Conference on Computer Vision and Pattern Recognition, 17-22 June, Minneapolis, Minnesota, pp. 1-7.
19. Persson, Å., J. Holmgren, and U. Söderman, 2002. Detecting and measuring individual trees using an Airborne Laser Scanner, Photogrammetric Engineering & Remote Sensing, 68(9):925-932. (Pubitemid 34979281)
20. Pollock, R.J., 1999. Individual tree recognition based on a synthetic tree crown image model, Proceedings of the International Forum on Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry (D.A. Hill and D.G. Leckie, editors), 10-12 February 1998, Victoria, British Columbia, Canadian Forest Service, Pacific Forestry Centre, pp. 25-34.
21. Pouliot D.A., and D.J. King, 2005. Approaches for optimal automated individual tree crown detection in regenerating coniferous forests, Canadian Journal of Remote Sensing, 31:255-267.
22. Pouliot, D.A., D.J. King, F.W. Bell, and D.G. Pitt, 2002. Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration, Remote Sensing of Environment, 82(2):322-334.
23. Pouliot, D.A., D.J. King, and D.G. Pitt, 2005, Development and evaluation of an automated tree Detection - Delineation algorithm for monitoring regenerating coniferous forests, Canadian Journal of Forest Research, 35:2332-2345.
24. Quackenbush, L.J., P.F. Hopkins, and G.J. Kinn, 2000. Using template correlation to identify individual trees in high resolution imagery, Proceedings of the 2000 ASPRS Annual Conference, 22-26 May, Washington, D.C., American Society for Photogrammetry and Remote Sensing, Bethesda, Maryland, unpaginated CD-ROM.
25. Tsai, A., A.J. Yezzi, C. Tempany, D. Tucker, A. Fan, W.E.L. Grimson, and A.S. Willsky, 2003. A shape-based approach to the segmentation of medical imagery using level sets, IEEE Transactions on Medical Imaging, 22(2):137-154.
26. Walsworth, N.A., and D.J. King, 1999. Comparison of two tree apex delineation techniques, Proceedings of the International Forum on Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry (D.A. Hill and D.G. Leckie, editors), 10-12 February 1998, Victoria, British Columbia, Canada, pp. 93-104.
27. Wang, L., P. Gong, and G.S. Biging, 2004. Individual tree-crown delineation and treetop detection in high-spatial resolution aerial imagery, Photogrammetric Engineering & Remote Sensing, 70(3):351-357.
28. Wulder, M., K.O. Niemann, and D.G. Goodenough, 2000. Local maximum filtering for the extraction of tree locations and basal area from high spatial resolution imagery, Remote Sensing of Environment, 73(1):103-114. (Pubitemid 30446188)
29. Xu, C., D. Pham, and J. Prince, 2000. Image segmentation using deformable models, Handbook of Medical Imaging, Volume 2: Medical Image Processing and Analysis (J. Fitzpatrick and M. Sonka, editors), SPIE Press, London, United Kingdom, pp. 129-174.