DETECTION of CITRUS TREES from UAV DSMS

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Volumn 4, Issue 1W1, 2017, Pages 27-34

  Ok, A O ^a   Ozdarici Ok, A ^a  

^a Faculty of Arts and Sciences   (Turkey)

Author keywords

Automated Detection; Citrus Trees; DSM; Local Maxima; Radial Symmetry; UAVs

Indexed keywords

EID: 85044504333     PISSN: 21949042     EISSN: 21949050     Source Type: Conference Proceeding    
DOI: 10.5194/isprs-annals-IV-1-W1-27-2017     Document Type: Conference Paper

References (46)

1. Dalponte, M., Orka, H. O., Ene, L. T., Gobakken, T., and Næsset, E., 2014. Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data. Remote Sensing of Environment, 140(1), pp. 306-317.
2. Dalponte, M., Reyes, F., Kandare, K., and Gianelle, D., 2015a. Delineation of individual tree crowns from ALS and hyperspectral data: a comparison among four methods. European Journal of Remote Sensing, 48(8), pp. 365-382.
3. Dalponte, M., Ene, L. T., Marconcini, M., Gobakken, T., and Næsset, E., 2015b. Semi-supervised SVM for individual tree crown species classification. ISPRS Journal of Photogrammetry and Remote Sensing, 110(12), pp. 77-87.
4. Ene, L., Næsset, E., and Gobakken, T., 2012. Single tree detection in heterogeneous boreal forests using airborne laser scanning and area-based stem number estimates. International Journal of Remote Sensing, 33(16), pp. 5171-5193.
5. Eysn, L., Hollaus, M., Lindberg, E., Berger, F., Monnet, J.-M., Dalponte, M., Kobal, M., Pellegrini, M., Lingua, E., Mongus, D., and Pfeifer, N., 2015. A benchmark of lidar-based single tree detection methods using heterogeneous forest data from the alpine space. Forests, 6(5), pp. 1721-1747.
6. Falkowski, M. J., Smith, A. M. S., Hudak, A. T., Gessler, P. E., Vierling, L. A., and Crookston, N. L., 2006. Automated estimation of individual conifer tree height and crown diameter via two-dimensional spatial wavelet analysis of lidar data. Canadian Journal of Remote Sensing, 32(2), pp. 153-161.
7. Farid, H., Simoncelli, E. P., 2004. Differentiation of discrete multidimensional signals. IEEE Transactions on Image Processing, 13(4), pp. 496-508.
8. Fassnacht, F. E., Latifi, H., Stereczak, K., Modzelewska, A., Lefsky, M., Waser, L. T., Straub, C., and Ghosh, A., 2016. Review of studies on tree species classification from remotely sensed data. Remote Sensing of Environment, 186(1), pp. 64-87.
9. Fieber, K. D., Davenport, I. J., Ferryman, J. M., Gurney, R. J., Walker, J. P., and Hacker, J. M., 2013. Analysis of full-waveform LiDAR data for classification of an orange orchard scene. ISPRS Journal of Photogrammetry and Remote Sensing, 82, pp. 63-82.
10. Geusebroek, J. M., Smeulders, A. W. M. and van de Weijer, J., 2003. Fast anisotropic gauss filtering. IEEE Transactions on Image Processing, 12(8), pp. 938-943.
11. Hada E., Estornell, J., 2016. Accuracy of tree geometric parameters depending on the LiDAR data density. European Journal of Remote Sensing, 49(3), pp. 73-92.
12. Heinzel, J. N., Weinacker, H., and Koch, B., 2011. Prior-knowledge-based single-tree extraction. International Journal of Remote Sensing, 32(17), pp. 4999-5020.
13. Hirschmugl, M., Ofner, M., Raggam, J., and Schardt, M., 2007. Single tree detection in very high resolution remote sensing data. Remote Sensing of Environment, 110(4), pp. 533-544.
14. Hirschmüller, H., 2008. Stereo processing by semi-global matching and mutual information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2), pp. 328-341.
15. Hung, C., Bryson, M., and Sukkarieh, S., 2012. Multi-class predictive template for tree crown detection. ISPRS Journal of Photogrammetry and Remote Sensing, 68, pp. 170-183.
16. Hyyppa, J., Kelle, O., Lehikoinen, M., and Inkinen, M., 2001. A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE Transactions on Geoscience and Remote Sensing, 39(5), pp. 969-975.
17. Jing, L., Hu, B., Noland, T., and Li, J., 2012. An individual tree crown delineation method based on multi-scale segmentation of imagery, ISPRS Journal of Photogrammetry and Remote Sensing, 70, pp. 88-98.
18. Kandare, K., Orka, H. O., Chan, J. C-W., and Dalponte, M., 2016. Effects of forest structure and airborne laser scanning point cloud density on 3D delineation of individual tree crowns. European Journal of Remote Sensing, 49, pp. 337-359.
19. Kwak, D.-A., Lee, W.-K., Lee, J.-H., Biging, G. S., and Gong, P., 2007. Detection of individual trees and estimation of tree height using LiDAR data. Journal of Forest Research, 12(6), pp. 425-434.
20. Li, D., Guo, H., Wang, C., Li, W., Chen, H., and Zuo, Z., 2016. Individual tree delineation in windbreaks using airborne-laser-scanning data and unmanned aerial vehicle stereo images. IEEE Geoscience and Remote Sensing Letters, 13(9), pp. 1330-1334.
21. Li, X., Shao, G., 2013. Object-based urban vegetation mapping with high-resolution aerial photography as a single data source, International Journal of Remote Sensing, 34(3), pp. 771-789.
22. Liu, T., Im, J., and Quackenbush, L. J., 2015. A novel transferable individual tree crown delineation model based on Fishing Net Dragging and boundary classification. ISPRS Journal of Photogrammetry and Remote Sensing, 110, pp. 34-47.
23. Leckie, D. G., Walsworth, N., and Gougeon, F. A., 2016. Identifying tree crown delineation shapes and need for remediation on high resolution imagery using an evidence based approach, ISPRS Journal of Photogrammetry and Remote Sensing, 114, pp. 206-227.
24. Loy, G., Zelinsky, A., 2003. Fast radial symmetry for detecting points of interest. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(8), pp. 959-973.
25. Malek, S., Bazi, Y., Alajlan, N., AlHichri, H., and Melgani, F., 2014. Efficient framework for palm tree detection in UAV images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(12), pp. 4692-4703.
26. Matsuki, T., Yokoya, N., and Iwasaki, A., 2015. Hyperspectral tree species classification of japanese complex mixed forest with the aid of lidar data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5), pp. 2177-2187.
27. Ok, A. O., and Baseski, E., 2015. Circular oil tank detection from panchromatic satellite images: a new automated approach. IEEE Geoscience and Remote Sensing Letters, 12(6), pp. 1347-1351.
28. Ok, A. O., and Ozdarici-Ok, A., 2017. 2-D delineation of individual citrus trees from UAV-based dense photogrammetric surface models, International Journal of Digital Earth, (revised version under review)
29. Ozdarici-Ok, A., 2015. Automatic detection and delineation of citrus trees from VHR satellite imagery. International Journal of Remote Sensing, 36(17), pp. 4275-4296.
30. Otsu, N., 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 9(1), pp. 62-66.
31. Ouma, Y. O., Tateishi, R., 2008. Urban-trees extraction from Quickbird imagery using multiscale spectex-filtering and non-parametric classification, ISPRS Journal of Photogrammetry and Remote Sensing, 63(3), pp. 333-351.
32. Paris, C., Valduga, D., and Bruzzone, L., 2016. A hierarchical approach to three-dimensional segmentation of LiDAR data at single-tree level in a multilayered forest. IEEE Transactions on Geoscience and Remote Sensing, 54(7), pp. 4190-4203.
33. Pix4D, 2016. https://pix4d. com (19 Jul. 2016).
34. Popescu, S. C., and Wynne, R. H., 2004. Seeing the trees in the forest: Using lidar and multispectral data fusion with local filtering and variable window size for estimating tree height. Photogrammetric Engineering and Remote Sensing, 70(5), pp. 589-604.
35. Shafri, H. Z. M., Hamdan, N., and Saripan, M. I., 2011. Semi-automatic detection and counting of oil palm trees from high spatial resolution airborne imagery, International Journal of Remote Sensing, 32(8), pp. 2095-2115.
36. Soille, P., 1999. Morphological Image Analysis: Principles and Applications, Springer-Verlag, pp. 170-171.
37. Smartplanes, 2016. http://smartplanes. se/ (19 Jul. 2016).
38. Swetnam, T. L., Falk, D. A., 2014. Application of Metabolic Scaling Theory to reduce error in local maxima tree segmentation from aerial LiDAR. Forest Ecology and Management, 323 (6), pp. 158-167.
39. TUIK., 2015. "Bitkisel Üretim statistikleri", http://www. Tuik. gov. Tr/PreTablo. do?alt-id=1001 (26. 01. 2017)
40. Vincent, L., 1993. Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Transactions on Image Processing, 2 (2), pp. 176-201.
41. Wallace, L., Lucieer, A., and Watson, C. S., 2014. Evaluating tree detection and segmentation routines on very high resolution UAV LiDAR data. IEEE Transactions on Geoscience and Remote Sensing, 52 (12), pp. 7619-7628.
42. Wang, Z., Boesch, R., and Ginzler, C., 2012. Forest delineation of aerial images with Gabor wavelets. International Journal of Remote Sensing, 33 (7), pp. 2196-2213.
43. Wolf (né Straub), B. M., Heipke, C., 2007. Automatic extraction and delineation of single trees from remote sensing data. Machine Vision and Applications, 18 (5), pp. 317-330.
44. Zhen, Z., Quackenbush, L. J., and Zhang, L., 2014. Impact of tree-oriented growth order in marker-controlled region growing for individual tree crown delineation using airborne laser scanner (ALS) data. Remote Sensing, 6 (1), pp. 555-579.
45. Zhen, Z., Quackenbush, L. J., Stehman, S. V., and Zhang, L., 2015. Agent-based region growing for individual tree crown delineation from airborne laser scanning (ALS) data. International Journal of Remote Sensing, 36 (7), pp. 1965-1993.
46. Zhen, Z., Quackenbush, L. J., and Zhang, L., 2016. Trends in automatic individual tree crown detection and delineation-evolution of LiDAR data. Remote Sensing, 8 (4), article number: 333.