Separation of Lanthanides/ Lanthanides and Actinides
SLOPE 1st workshop - presentation 4
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
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
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Because it’s fun !!
11. Mould growth on wooden surfaces
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Can we study the development of fungi colonies ?
Can we model the mould growth and predict it ?
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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
21. Weathering of wood surface
case study of thin samples (100 microns)
Observations in transmission mode
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Time series of weathered
thin samples
22. Mosaic of 12 thin samples
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23. Quality grading of logs in the forest (SLOPE)
Andreas Zitek + Boku team
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24. Hyperspectral imaging of logs in forest
• Varying moisture conditions
• Varying light conditions
–Use natural light or artificial ?
–White reference
• Surface roughness
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Challenges :
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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
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