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A novel Computer-Aided Tree Species Identification method based on Burst Wind Segmentation of 3D bark textures

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Abstract

Terrestrial Laser Scanning (TLS) systems have gained increasing popularity in the forestry domain and are today widely used for the automatic measurement of forest inventory attributes. Nevertheless, to the best of our knowledge the problem of tree species recognition from TLS data has received very little attention from the scientific community. It is in this context that we present a novel Computer-Aided Tree Species Identification method based on 3D bark texture analysis. The novelty of our approach resides in the following three key points: (1) 3D salient regions extraction using a new morphological segmentation method that we have called Burst Wind Segmentation, (2) the extraction and pre-annotation of a collection of typical 3D bark patterns, known as scars, from each of the tree species. The pre-annotated scars are stored in a dictionary that we have called ScarBook and they are used as a reference for the comparison of the 3D salient segmented regions, (3) a wide variety of advanced shape, saliency, curvature and roughness features are extracted from the 3D salient segmented regions. To study the performance of our method, an experiment has been carried out on a dataset composed of 969 patches which correspond to 30 cm long segments of the trunk at breast height. Six species among the most dominant species in European forests have been tested with patches of different diameter at breast height values so as to study the identification accuracy with respect to age. The results obtained are very encouraging and promising and they confirm the possibility of identifying tree species using TLS data.

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References

  1. Dassot, M., Constant, T., Fournier, M.: The use of terrestrial LiDAR technology in forest science: application fields, benefits et challenges. Ann. For. Sci. 68(5), 959–974 (2011)

    Article  Google Scholar 

  2. Othmani, A., Piboule, A., Krebs, M., Stolz, C., Voon, L.L.Y.: Towards automated et operational forest inventories with t-lidar. In: Silvilaser 2011 Conference Proceedings (2011)

  3. Reulke, R., Haala, N.: Tree species recognition with fuzzy texture parameters. IWCIA 2004 and Lecture Notes Computer Sciences (2004)

  4. Haala, N., Reulke, R., Thies, M., Aschoff, T.: Combination of terrestrial laser scanning with high resolution panoramic images for investigations in forest applications and tree species recognition. In: ISPRS working group V/1, Panoramic Photogrammetry Workshop (2004)

  5. Boman, J., Borlin, N.: Tree species classification using terrestrial photogrammetry. In: Master thesis report (2013)

  6. Othmani, A., Lomenie, N., Piboule, A., Stolz, C., Voon, L.L.Y.: Region-based segmentation on depth images from a 3D reference surface for tree species recognition. In: IEEE International Conference on Image Processing (2013)

  7. Othmani, A., Piboule, A., Voon, L.L.Y.: Hybrid segmentation of depth images watershed and region merging based method for tree species recognition. In: 11th IEEE IVMSP workshop: 3D Image/Video Technologies and Applications (2013)

  8. Othmani, A., Voon, L.L.Y., Stolz, C., Piboule, A.: Single tree species classification from terrestrial laser scanning data for forest inventory. J. Pattern Recogn. Lett. 34(16), 2144–2150 (2013)

    Article  Google Scholar 

  9. Jebara, T.: Kernelizing sorting, permutation, and alignment for minimum volume PCA. In: Learning Theory, pp. 609–623. Springer, Heidelberg (2004)

  10. Barequet, G., Har-Peled, S.: Efficiently approximating the minimum-volume bounding box of a point set in three dimensions. J. Algorithms 38(1), 91–109 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  11. Tedjokusumo, J., Leow, W.K.: Normalization and alignment of 3D objects based on bilateral symmetry planes. In: Advances in Multimedia Modeling, pp. 74–85. Springer, Heidelberg (2006)

  12. Yinyu, Y.: Interior Point Algorithms: Theory and Analysis. Wiley, Amsterdam (2011)

    MATH  Google Scholar 

  13. Agathos, A., Pratikakis, I., Perantonis, S., Sapidis, N., Azariadis, P.: 3D mesh segmentation methodologies for CAD applications. In: Computer-Aided Design, pp. 827–841 (2007)

  14. Mangan, A.P., Whitaker, R.T.: Surface segmentation using morphological watersheds. In: Proceedings of IEEE Visualization 1998 Late Breaking Hot Topics (1998)

  15. Page, D.L., Koschan, A.F., Abidi, M.A.: Perception based 3d triangle mesh segmentation using fast marching watersheds. In: Proceedings of International Conference on Computer Vison and Pattern Recognition, Vol. II, pp. 27–32 (2003)

  16. Lai, Y.K., Hua, S.M., Gu., X., Martin, R.R.: Geometric texture synthesis and transfer via geometry images. In: Proceedings of ACM Symposium on Solid and Physical Modeling (2005)

  17. Lavoue, G., Dupont, F., Baskurt, A.: A new CAD mesh segmentation method, based on curvature tensor analysis. Comput. Aided Des. 37(10), 975–987 (2005)

    Article  MATH  Google Scholar 

  18. Fairfield, J.: Toboggan contrast enhancement for contrast segmentation. In: Proceedings of IEEE 10th International Conference on Pattern Recognition, vol. 1, pp. 712–716 (1990)

  19. Mortensen, E.N., Barrett, W.A.: Toboggan-based intelligent scissors with a four-parameter edge model. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 452–458 (1999)

  20. Lin, Y.C., Tsai, Y.P., Hung, Y.P., Shih, Z.C.: Comparison between immersion-based and toboggan-based watershed image segmentation. IEEE Trans. Image Process. 15(3), 632–640 (2006)

    Article  Google Scholar 

  21. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: The 2nd International Conference on Knowledge Discovery and Data Mining (1996)

  22. Okamura, A.M., Cutkosky, M.R.: Haptic exploration of fine surface features. In: IEEE International Conference on Robotics and Automation (1999)

  23. Petitpas, B., Beaudoin, L., Rudant, J.P.: Roughness measurement from multi-stereo reconstruction. In: Photogrammetric Computer Vision and Image Analysis (2010)

  24. Weinberger, K.Q., Saul, L.K.: Unsupervised learning of image manifolds by semidefinite programming. Int. J. Comput. Vis. 70(1), 77–90 (2006)

    Article  Google Scholar 

  25. Edelsbrunner, H., Mucke, E.P.: Three-dimensional alpha shapes. ACM Trans. Graph. 13(1), 43–72 (1994)

    Article  MATH  Google Scholar 

  26. Gastaud, M., Barlaud, M., Aubert, G.: Combining shape prior and statistical features for active contour segmentation. In: IEEE Transactions on Circuits and Systems for Video Technology (2004)

  27. Robert, V.U., Ingmar, B.: Subvoxel precise skeletons of volumetric data base on fast marching methods. Med. Phys. 34(2), 627–638 (2007)

    Article  Google Scholar 

  28. Olsen, M.A., Hartungx, D., Busch, C., Larsenz, R.: Convolution approach for feature detection in topological skeletons obtained from vascular patterns. In: IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM) (2011)

  29. Yang, M., Kpalma, K., Ronsin, J.: A survey of shape feature extraction techniques. Pattern Recogn. 43–90 (2008)

  30. Rubner, Y., Tomasi, C., Guibas, L.J.: The earth movers distance as a metric for image retrieval. Int. J. Comput. Vis. 40(2), 99–121 (2000)

    Article  MATH  Google Scholar 

  31. Jeng-Shyang, P., Hao, L., Zhe-Ming, L., Chang, J.-C.H. : A new 3D shape descriptor based on rotation. In: The 6th International Conference on Intelligent Systems Design and Applications, Vol. 02, pp. 300–306. Jinan, China (2006)

  32. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  33. Genuer, R., Poggi, J.M., Tuleau-Malot, C.: Variable selection using random forests. Pattern. Recogn. Lett. 31(14), 2225–2236 (2010)

    Article  Google Scholar 

  34. Lai, K., Bo, L., Ren, X., Fox, D.: Detection-based object labeling in 3D Scenes. In: IEEE International Conference on on Robotics and Automation (2012)

  35. Aijazi, A.K., Checchin, A., Trassoudaine, L.: Segmentation based classification of 3D urban point clouds: a super-voxel based approach with evaluation. Remote Sens. 5(4), 1624–1650 (2013)

    Article  Google Scholar 

  36. Strom, J.H., Richardson, A., Olson, E.: Graph-based segmentation for colored 3D laser point clouds. IROS, pp. 2131–2136 (2010)

  37. Zhan, Q., Liang, Y., Xiao, Y.: Color-based segmentation of point clouds. In: Bretar, F., Pierrot-Deseilligny, M., Vosselman, G. (Eds.) Laser scanning 2009, IAPRS, Vol. XXXVIII, Part 3/W8 Paris, France, 1–2 Sept 2009

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Authors and Affiliations

  1. ISIT, Université d’Auvergne, Clermont Université, BP 10448, 63000, Clermont-Ferrand, France

    Alice Ahlem Othmani & Jean-Marie Favreau

  2. Le2i UMR-CNRS 6306, Université de Bourgogne, Le Creusot, France

    Alice Ahlem Othmani, Cansen Jiang & Lew F. C. Lew Yan Voon

  3. Département R&D, Office National des Forêts, 54000, Nancy, France

    Alice Ahlem Othmani & Alexandre Piboule

  4. LIPADE, Université Paris Descartes, Paris, France

    Nicolas Lomenie

Authors
  1. Alice Ahlem Othmani
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  2. Cansen Jiang
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  3. Nicolas Lomenie
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  4. Jean-Marie Favreau
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  5. Alexandre Piboule
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  6. Lew F. C. Lew Yan Voon
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Corresponding author

Correspondence to Alice Ahlem Othmani.

Additional information

This work is financially supported by the “Conseil Regional de Bourgogne” under contract Nos. 2010 9201AAO048S06469 and 2010 9201CPERO007S06470, and the “Office National des Forêts”.

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Othmani, A.A., Jiang, C., Lomenie, N. et al. A novel Computer-Aided Tree Species Identification method based on Burst Wind Segmentation of 3D bark textures. Machine Vision and Applications 27, 751–766 (2016). https://doi.org/10.1007/s00138-015-0738-2

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  • DOI: https://doi.org/10.1007/s00138-015-0738-2

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