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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