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Pine and spruce roundwood species classification using multivariate image analysis on bark
Umeå University, Faculty of Science and Technology, Department of Chemistry.
Umeå University, Faculty of Science and Technology, Department of Chemistry.
2005 (English)In: Holzforschung, ISSN 0018-3830, E-ISSN 1437-434X, Vol. 59, no 6, 689-695 p.Article in journal (Refereed) Published
Abstract [en]

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%.

Place, publisher, year, edition, pages
Berlin: Walter de Gruyter , 2005. Vol. 59, no 6, 689-695 p.
Keyword [en]
bark, co-occurrence matrix, multivariate image analysis, partial least squares projection to latent structures (PLS), pine, spruce, wavelet, wood species
National Category
Chemical Sciences
Identifiers
URN: urn:nbn:se:umu:diva-4724DOI: 10.1515/HF.2005.110OAI: oai:DiVA.org:umu-4724DiVA: diva2:143946
Available from: 2005-10-06 Created: 2005-10-06 Last updated: 2017-12-14Bibliographically approved
In thesis
1. Prediction of wood species and pulp brightness from roundwood measurements
Open this publication in new window or tab >>Prediction of wood species and pulp brightness from roundwood measurements
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis presents a number of studies, where a multivariate approach was taken to construct models that predict wood species and thermo mechanical pulp brightness from roundwood of Norway spruce and Scots pine. The first and second studies produced multivariate prediction models for wood species from the bark of spruce and pine. These models can be used for wood species classification and would replace the manual log assessment that takes place today. Principal Component Analysis, PCA, and Partial least squares projections to Latent Structures, PLS, were used to predict the wood species from multivariate measurements recorded from the bark of spruce and pine. Two different kinds of measurements were employed, near-infrared spectroscopy and digital imaging. Both methods showed that it was possible to predict the wood species with a high accuracy.

The third and fourth studies of the thesis are related to the wood storage of roundwood and the deterioration of wood that occurs during the storage. The third study used an experimental design with five storage factors that provided different conditions for the analysed wood. The experimental design made it possible to identify the factors and the interaction between factors, which were important for the ISO brightness of peroxide and dithionite bleached thermo mechanical pulp, TMP. The final study of the thesis used NIR spectroscopy for predicting the ISO brightness of bleached TMP. Spectra recorded from stored wood were used to construct PLS prediction models.

Place, publisher, year, edition, pages
Umeå: Kemi, 2005. 72 p.
Keyword
Design of Experiments, ISO brightness, Multivariate Image Analysis, Multivariate Modelling, NIR spectroscopy, Norway spruce, PCA, PLS, Roundwood, Scots pine
National Category
Organic Chemistry
Identifiers
urn:nbn:se:umu:diva-605 (URN)91-7305-959-9 (ISBN)
Public defence
2005-10-28, KB3A9, KBC, Umeå Universitet, SE-90187, Umeå, 10:00 (English)
Opponent
Supervisors
Available from: 2005-10-06 Created: 2005-10-06 Last updated: 2011-03-17Bibliographically approved

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