Bayesian object recognition for the analysis of complex forest scenes in airborne laser scanner data

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Volumn 34, Issue , 2002, Pages

  Andersen, Hans Erik ^a   Reutebuch, Stephen E ^b   Schreuder, Gerard F ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

^b PACIFIC NORTHWEST RESEARCH STATION   (United States)

Author keywords

Forestry; Laser scanning; LIDAR; Object; Recognition; Remote sensing; Statistics

Indexed keywords

BAYESIAN NETWORKS; COMPUTER VISION; COMPUTERIZED TOMOGRAPHY; FORESTRY; INTELLIGENT SYSTEMS; LASER APPLICATIONS; MARKOV PROCESSES; MONTE CARLO METHODS; OPTICAL RADAR; PHOTOGRAMMETRY; REMOTE SENSING; SPACE OPTICS; STATISTICS;

AIRBORNE LASER SCANNING; LASER SCANNING; MARKOV CHAIN MONTE CARLO SIMULATION; OBJECT; OBJECT RECOGNITION ALGORITHM; RECOGNITION; REMOTE SENSING TECHNOLOGY; THREE-DIMENSIONAL ANALOGUES;

OBJECT RECOGNITION;

EID: 84956546566     PISSN: 16821750     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Andersen, H., S. Reutebuch, and G. Schreuder, 2001. Automated individual tree measurement through morphological analysis of a LIDAR-based canopy surface model. In: Proceedings of the First International Precision Forestry Symposium, Seattle, WA, USA.
2. Baddeley, A., and M. van Lieshout, 1993. Stochastic geometry models in high-level vision. In: Mardia, K.V. and G.K. Kanjii, eds. Advances in Applied Statistics 1. Carfax, Abingdon, Oxfordshire, pp. 231-256.
3. Besag, J., 1993. Towards Bayesian image analysis. In: Mardia, K.V. and G.K. Kanjii, eds. Advances in Applied Statistics 1. Carfax, Abingdon, Oxfordshire, pp. 107-119.
4. Dralle, K., and M. Rudemo, 1997. Automatic estimation of individual tree positions from aerial photos. Canadian Journal of Forest Research, 27, pp. 1728-1736.
5. Goudriaan, J., 1988. The bare bones of leaf-angle distribution in radiation models for canopy photosynthesis and energy exchange. Agricultural and Forest Meteorology 43, pp. 155-169.
6. Green, P., 1995. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4), pp. 711-732.
7. Gulden, R., 2000. Remote sensing at the dawn of a new millennium: A Washington DC perspective. In: Proceedings of the Eighth Biennial Remote Sensing Applications Conference, Albuquerque, NM, USA.
8. Kirkpatrick, S., C.D. Gelatt, Jr., and M.P. Vecchi, 1983. Optimization by simulated annealing. Science, 220(4598), pp. 671-680.
9. Larsen, M., 1998. Finding an optimal match window for spruce top detection based upon an optical tree model. In: Proceedings of the International Forum on Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry, Victoria, BC, Canada.
10. Lund, J. and M. Rudemo, 2000. Models for point processes observed with noise. Biometrika, 87(2), pp. 235-249.
11. Magnussen, S., P. Eggermont, and V.N. LaRiccia, 1999. Recovering tree heights from airborne laser scanner data. Forest Science, 45(3), pp. 407-422.
12. Means, J., S. Acker, B. Fitt, M. Renslow, L. Emerson, and C. Hendrix, 2000. Predicting forest stand characteristics with airborne scanning LIDAR. Photogrammetric Engineering and Remote Sensing, 66, pp. 1367-1371.
13. Nelson, R., W. Krabill and J. Tonelli, 1988. Estimating forest biomass and volume using airborne laser data. Remote Sensing of the Environment, 24, pp. 247-267.
14. Pollock, R., 1996. The automatic recognition of individual trees in aerial images of forests based upon a synthetic tree crown image model. PhD thesis, Department of Computer Science, University of British Columbia, Vancouver, BC, Canada.
15. Ripley, B., 1981. Spatial Statistics. Wiley, New York.
16. Ripley, B., 1991. The use of spatial models as image priors. In: Antonio Possolo, ed., Spatial Statistics and Imaging, vol. 20 of Lecture Notes – Monograph Series, Institute of Mathematical Statistics, Hayward, CA, USA.
17. Rue, H., and M. Hurn, 1999. Bayesian object recognition. Biometrika, 86(3), pp. 649-660.
18. Rue, H., and A. Syversveen, 1998. Bayesian object recognition with Baddeley’s delta loss. Advances in Applied Probability, 30, pp. 64-84.
19. Sheng, Y., P. Gong, and G. Biging, 2001. Model-based conifer-crown surface reconstruction from high-resolution aerial images. Photogrammetric Engineering and Remote Sensing, 67(8), pp. 957-965.
20. Sun, G., and K. Ranson, 2000. Modeling lidar returns from forest canopies. IEEE Transactions on Geoscience and Remote Sensing 38(6), pp. 2617-2626.
21. Vanderbilt, V.C., L.F. Silva, and M.E. Bauer, 1990. Canopy architecture measured with a laser. Applied Optics, 29(1), pp. 99-106.
22. van Lieshout, M., 1994. Stochastic annealing for nearest-neighbour point processes with application to object recognition. Advances in Applied Probability, 26, 281-300.
23. van Lieshout, M., 1995. Stochastic geometry models in image analysis and spatial statistics. CWI, Amsterdam, the Netherlands.
24. Webb, W. and M. Ungs, 1993. Three-dimensional distribution of needle and stem surface area in a Douglas-fir. Tree Physiology 13, pp. 203-212.