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Hyperspectral imaging on woodIngunn Burud,
NORWEGIAN UNIVERSITY OF LIFE SCIENCESAndreas Flø, Lone R. Gobakken, Thomas Thiis, Anna Sandak, Jakub
Sandak
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Hyperspectral cameras at IMT (Department of Mathematical Sciences and Technology)
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VIS 300-1000 nm (Specim)NIR 1000-2500 nm (Specim)NIR 900-1700 nm (NEO)
Laboratory measurements
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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
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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 !!
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Astrup Fearnley Museum of Modern Art, Oslo2012. Cladding in aspen
Photo: www.expo-nova.no
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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
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Time series of mould growth on wood in lab - Parafac
How can we relate this to standard visual assessment ?
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PLS-DA Object detection Histogram of object perimeters
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Mould classified by PLS-DA
30 % mould
Object detection:6 objects with perimeter :1494, 1622, 256, 451, 142, 424
Outdoor case study
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• Painted spruce• Spruce• Heartwood• Aspen• Acetylated
White reference inside and outside
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North aspen
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22 Aug 20 Sept 9 Oct 24 Oct 8 Nov
PLS-DA
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24 Oct
8 Nov, modellfrom 24 Oct
24 Oct, modell from lab
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Weathering of wood surfacecase study of thin samples (100 microns)Observations in transmission mode
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Time series of weathered thin samples
Mosaic of 12 thin samples
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Quality grading of logs in the forest (SLOPE)Andreas Zitek + Boku team
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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 :
Influence of surface roughness
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PLS-DA
Classes : early wood, late wood, bark, rotClasses : early wood, late wood, bark, background
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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TETRACAMMini-MCA
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Thanks for your attention