This document summarizes research on using hyperspectral imaging to analyze wood. It discusses using hyperspectral cameras to capture spectral information from wood samples across multiple wavelengths. Researchers are aiming to identify chemical properties of wood, like lignin and cellulose content, and monitor changes over time, such as the development of mould on wood samples in the lab. The document also discusses challenges with applying this technique outdoors to analyze painted and weathered wood samples, as well as logs in forest environments, where varying moisture, light and surface roughness conditions exist. The goal is to select important bands of wavelengths that provide useful information and could enable hyperspectral cameras to be used for in-line monitoring applications.

1. Norwegian University of Life Sciences 1
Hyperspectral imaging on wood
Ingunn Burud,
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Andreas Flø, Lone R. Gobakken, Thomas Thiis, Anna Sandak, Jakub
Sandak

2. Norwegian University of Life Sciences
Norwegian University of Life Sciences 2

3. Hyperspectral cameras at IMT
(Department of Mathematical Sciences
and Technology)
3
VIS 300-1000 nm (Specim)
NIR 1000-2500 nm (Specim)
NIR 900-1700 nm (NEO)

4. Laboratory measurements
Norwegian University of Life Sciences 4

5. Norwegian University of Life Sciences 5

6. Wood
• Used in numerous applications : constructions, furniture,
paper, bioenergy, …
• A complex matrix of several polymers : lignin, cellulose,
hemicellulose, extractives and minerals
• Heterogeneous and anisotropic
• Hygroscopic material, responding to humidity changes in
the surrounding air
Norwegian University of Life Sciences 6

7. Why hyperspectral imaging ?
• More than the eye can see
–Chemical properties can be derived, depending on
wavelength (lignin, cellulose)
• Spectral information + spatial information
• Development of new technologies for sensors that can be
applied in-line
–Band selection to define important wavelengths
–Spectral library for calibration of outdoor
measurements from multispectral cameras
Norwegian University of Life Sciences 7
Because it’s fun !!

8. Norwegian University of Life SciencesTittel på presentasjon 8

9. Astrup Fearnley Museum of Modern Art,
Oslo
2012. Cladding in aspen
Photo: www.expo-nova.no

10. Norwegian University of Life Sciences 10

11. Mould growth on wooden surfaces
11
Can we study the development of fungi colonies ?
Can we model the mould growth and predict it ?

12. Norwegian University of Life Sciences 12
Hyperspectral measurements
Time series of mould growth on wood in lab

13. Norwegian University of Life Sciences 13
Time series of mould growth on wood in lab - Parafac

14. How can we relate this to
standard visual assessment ?
Norwegian University of Life Sciences 14
PLS-DA Object detection Histogram of object
perimeters

15. Norwegian University of Life Sciences 15
Mould classified
by PLS-DA
30 % mould
Object detection:
6 objects with perimeter :
1494, 1622, 256, 451, 142, 424

16. Outdoor case study
16
• Painted spruce
• Spruce
• Heartwood
• Aspen
• Acetylated

17. White reference inside and outside
Norwegian University of Life Sciences 17

18. North aspen
18
22 Aug 20 Sept 9 Oct 24 Oct 8 Nov

19. PLS-DA
Norwegian University of Life Sciences 19
24 Oct
8 Nov, modell
from 24 Oct
24 Oct, modell from lab

20. Norwegian University of Life Sciences 20

21. Weathering of wood surface
case study of thin samples (100 microns)
Observations in transmission mode
Norwegian University of Life Sciences 21
Time series of weathered
thin samples

22. Mosaic of 12 thin samples
Norwegian University of Life Sciences 22

23. Quality grading of logs in the forest (SLOPE)
Andreas Zitek + Boku team
Norwegian University of Life Sciences 23

24. Hyperspectral imaging of logs in forest
• Varying moisture conditions
• Varying light conditions
–Use natural light or artificial ?
–White reference
• Surface roughness
Norwegian University of Life Sciences 24
Challenges :

25. Influence of surface roughness
Norwegian University of Life Sciences 25

26. Norwegian University of Life Sciences 26
PLS-DA
Classes : early wood, late wood, bark, rotClasses : early wood, late wood, bark,
background

27. Band selection
• A number of known interesting wavelength bands
–e.g., lignin, cellulose, decay, bark
• Research to find selected bands that will cover what we
are interested in
• Hyperspectral  Multispectral
Norwegian University of Life Sciences 27

28. TETRACAM
Mini-MCA
Norwegian University of Life SciencesTittel på presentasjon 28

29. Norwegian University of Life Sciences
29
Thanks for your
attention