Home Pine and spruce roundwood species classification using multivariate image analysis on bark
Article
Licensed
Unlicensed Requires Authentication

Pine and spruce roundwood species classification using multivariate image analysis on bark

  • David Nilsson and Ulf Edlund
Published/Copyright: November 1, 2005
Become an author with De Gruyter Brill
Holzforschung
From the journal Volume 59 Issue 6

Abstract

Wood discs from 67 pine and 79 spruce logs were collected from a forest clearing. Three different 24-bit red-green-blue (RGB) images were acquired from the radial surface of each disc. The first image contained bark, the second image was a mixture of bark and wood surface, and the third image consisted only of wood surface. The image texture was compressed into vectors of Fourier-transformed wavelet coefficients. These were assembled in matrices and analysed by principal component analysis (PCA) and partial least-squares projections to latent structures (PLS). Classification using Fourier-transformed wavelet scales showed that the wood species could be predicted with 90% accuracy. A thorough examination of this classification showed that the predicting power of these models was mostly due to wavelet scales that represented the mean value of each colour channel. The prediction accuracy that could be obtained from coefficients representing image texture was generally low. The use of grey-level co-occurrence matrices prior to the wavelet transformation showed, however, that it is possible to classify the wood species of pine and spruce with an accuracy approaching 100%.

:

Corresponding author. Department of Chemistry, Organic Chemistry, Umeå University, SE-901 87 Umeå, Sweden

References

Antonelli, A., Cocchi, M., Fava, P., Foca, G., Franchini, C.G., Ulrici, A. (2004) Automated evaluation of food colour by means of multivariate image analysis coupled to a wavelet-based classification algorithm. Anal. Chim. Acta515:3–13.10.1016/j.aca.2004.01.005Search in Google Scholar

Basset, O., Buquet, B., Abouelkaram, S., Delachartre, P., Culioli, J. (2000) Application of texture image analysis for the classification of bovine meat. Food Chem.69:437–445.10.1016/S0308-8146(00)00057-1Search in Google Scholar

Bharati, M.H., Liu, J.J., MacGregor J.F. (2004) Image texture analysis: methods and comparisons. Chemomet. Intell. Lab. Syst.72:57–71.10.1016/j.chemolab.2004.02.005Search in Google Scholar

Brunner, M., Eugster, R., Trenka, E., Bergamin-Strotz, L. (1996) FT-NIR spectroscopy and wood identification. Holzforschung50:130–134.10.1515/hfsg.1996.50.2.130Search in Google Scholar

Daubechies, I. Ten Lectures on Wavelets. SIAM, Philadelphia, 1992.10.1137/1.9781611970104Search in Google Scholar

Esbensen, K., Geladi, P. (1989) Strategy of multivariate image-analysis (MIA). Chemomet. Intell. Lab. Syst.7:67–86.10.1016/0169-7439(89)80112-1Search in Google Scholar

Funck, J.W., Zhong, Y., Butler, D.A., Brunner, C.C., Forrer, J.B. (2003). Image segmentation algorithms applied to wood defect detection. Comput. Electron. Agric.41:157–179.Search in Google Scholar

Geladi, P., Esbensen, K. (1991) Regression on multivariate images – Principal component regression for modeling, prediction and visual diagnostic tools. J. Chemomet.5:97–111.10.1002/cem.1180050206Search in Google Scholar

Geladi P., Kowalski, B.R. (1986) Partial least squares regression: A tutorial. Anal. Chim. Acta185:1–17.10.1016/0003-2670(86)80028-9Search in Google Scholar

Geladi, P., Isaksson, H., Lindqvist, L., Wold, S., Esbensen, K. (1989) Principal component analysis of multivariate images. Chemomet. Intell. Lab. Syst.5:209–220.10.1016/0169-7439(89)80049-8Search in Google Scholar

Gjerdrum, P., Warensjö, M., Nylinder, M. (2001) Classification of crook types for unbarked Norway spruce sawlogs by means of a 3D log scanner. Holz Roh Werkstoff59:374–379.10.1007/s001070100228Search in Google Scholar

Gonzalez R.C., Woods R.E. Digital Image Processing, 2nd ed. Prentice Hall, New Jersey, 2002.Search in Google Scholar

Hagman, P.O.G. (1993) Automatic quality sorting of Picea abies logs with a gamma-ray log scanner. Scand. J. For. Res.8:583–590.10.1080/02827589309382804Search in Google Scholar

Haralick, R.M., Shanmugam, K., Dinstein, I. (1973) Textural features for image classification. IEEE Trans. Syst. Man Cybernetics3:610–621.10.1109/TSMC.1973.4309314Search in Google Scholar

Jain, A.K., Mao, J.C., Mohiuddin, K.M. (1996) Artificial neural networks: A tutorial. Computer29:31–44.10.1109/2.485891Search in Google Scholar

Jordan, R., Feeney, F., Nesbitt, N., Evertsen, J.A. (1998) Classification of wood species by neural network analysis of ultrasonic signals. Ultrasonics36:219–222.10.1016/S0041-624X(97)00148-0Search in Google Scholar

Kvaal, K., Wold, J.P., Indahl, U.G., Baardseth, P., Næs, T. (1998) Multivariate feature extraction from textural images of bread. Chemomet. Intell. Lab. Syst.42:141–158.10.1016/S0169-7439(98)00017-3Search in Google Scholar

Lawrence, A.H. (1989) Rapid characterization of wood species by ion mobility spectrometry. J. Pulp Pap. Sci.15:J196–J199.Search in Google Scholar

Lewis, I.R., Daniel, N.W., Chaffin, N., Griffiths, P. (1994) Raman spectrometry and neural networks for the classification of wood types 1. Spectrochim. Acta A Mol. Spectrosc.50:1943–1958.10.1016/0584-8539(94)80207-6Search in Google Scholar

Lo, S.C.B., Li, H., Freedman, M.T. (2003) Optimization of wavelet decomposition for image compression and feature preservation. IEEE Trans. Med. Imaging22:1141–1151.10.1109/TMI.2003.816953Search in Google Scholar

Mäenpää, T., Pietikainen, M. (2004) Classification with colorand texture: jointly or separately? Pattern Recogn.37:1629–1640.Search in Google Scholar

Oja, J., Wallbäcks, L., Grundberg, S., Hägerdal, E., Grönlund, A. (2003) Automatic grading of Scots pine (Pinus sylvestris L.) sawlogs using an industrial X-ray log scanner. Comput. Electron. Agric.41:63–75.Search in Google Scholar

Thygesen, L.G., Lundqvist, S.O. (2000) NIR measurement of moisture content in wood under unstable temperature conditions. Part 1. Thermal effects in near infrared spectra of wood. J. Near Infrared Spectrosc.8:183–189.Search in Google Scholar

Trygg, J., Wold, S. (2002) Orthogonal projections to latent structures, O-PLS. J. Chemomet.16:119–128.10.1002/cem.695Search in Google Scholar

Van de Wouwer, G., Scheunders, P., Livens, S., Van Dyck, D. (1999) Wavelet correlation signatures for color texture characterization. Pattern Recogn.32:443–451.10.1016/S0031-3203(98)00035-1Search in Google Scholar

Wettimuny, R., Penumadu, D. (2004) Application of Fourier analysis to digital imaging for particle shape analysis. J. Comp. Civil Eng.18:2–9.10.1061/(ASCE)0887-3801(2004)18:1(2)Search in Google Scholar

Wold, S. (1978) Cross-validatory estimation of the number of components in factor and principal components models. Technometrics20:397–405.10.1080/00401706.1978.10489693Search in Google Scholar

Wold S., Esbensen, K., Geladi, P. (1987) Principal component analysis. Chemomet. Intell. Lab. Syst.2:37–52.10.1016/0169-7439(87)80084-9Search in Google Scholar

Wold, S., Antti, H., Lindgren, F., Öhman, J. (1998) Orthogonal signal correction of near-infrared spectra. Chemomet. Intell. Lab. Syst.44:175–185.10.1016/S0169-7439(98)00109-9Search in Google Scholar

Published Online: 2005-11-01
Published in Print: 2005-11-01

©2005 by Walter de Gruyter Berlin New York

Articles in the same Issue

  1. Contents
  2. Species index (scientific names)
  3. Subject Index
  4. Acknowledgement
  5. Author Index
  6. Ultrastructural changes in a holocellulose pulp revealed by enzymes, thermoporosimetry and atomic force microscopy
  7. Development of wet strength additives from wheat gluten
  8. Characterization of electrolyzed magnesium spent-sulfite liquor
  9. Molecular weight-functional group relations in softwood residual kraft lignins
  10. Structure-activity relationships of cadinane-type sesquiterpene derivatives against wood-decay fungi
  11. Effect of water on wood liquefaction and the properties of phenolated wood
  12. Effect of wood species and molecular weight of phenolic resins on curing behavior and bonding development
  13. Contact-free measurement and non-linear finite element analyses of strain distribution along wood adhesive bonds
  14. Comparison between HT-dried and LT-dried spruce timber in terms of shape and dimensional stability
  15. Physical properties of earlywood and latewood of Pinus radiata D. Don: Anisotropic shrinkage, equilibrium moisture content and fibre saturation point
  16. Effect of stress levels on compressive low-cycle fatigue behaviour of softwood
  17. Comparison of morphological and chemical properties between juvenile wood and compression wood of loblolly pine
  18. Ultrastructure of commercial recycled pulp fibers for the production of packaging paper
  19. Oxalate regulation by two brown rot fungi decaying oxalate-amended and non-amended wood
  20. Pine and spruce roundwood species classification using multivariate image analysis on bark
  21. Detection and species discrimination using rDNA T-RFLP for identification of wood decay fungi
  22. Personalia
  23. Award presentation on the occasion of the 13th International Symposium on Wood, Fibre and Pulping Chemistry, May 16–19, 2005, Auckland, New Zealand
  24. NMR studies on Fraser fir Abies fraseri (Pursh) Poir. Lignins
Downloaded on 2.12.2025 from https://www.degruyterbrill.com/document/doi/10.1515/HF.2005.110/pdf
Scroll to top button