Use of hyperspectral imaging for artwork evaluation

Adam Polak1, Professor Stephen Marshall2, Dr David J. M. Stothard3

1 – Electronic and Electrical Engineering, University of Strathclyde, Glasgow (adam.polak@strath.ac.uk)

2 – Electronic and Electrical Engineering, University of Strathclyde, Glasgow

3 - Fraunhofer Centre for Applied Photonics, Fraunhofer UK Research Ltd

Hyperspectral Imaging

Initial classification results

Hyperspectral data cube is a stack of images in which each pixel

(vector) is represented by a spectral signature that characterizes

the underlying objects

Set of signal processing techniques is able to classify captured

objects based on their spectral signatures

Variety of hyperspectral imagers is available on the market,

characterised by different scanning methods and spectral ranges.

Firefly Imager - laser-based, mid-IR, active hyperspectral imaging

system is used in this project

Despite observed problems, classification was performed on available data

Intensity variation results in very poor classification performance

Artwork data collection Set of test paintings

with chosen paint

samples were

imaged in in the

near and mid

infrared spectral

range (from 1.5µm

to 3.7µm)

The outcomes of this project will guide the design and implementation of the next generation of hyperspectral

imagers for applications in the art world and beyond.

Nature Photonics 3, 627 - 629 (2009)

Intensity variation issue Beam deflection from the Imager scanning system caused changes in

collection efficiencies of reflected radiation. It has direct impact on spectral

signatures of imaged objects in different areas of the image

Low ---------- accuracy ---------- HighTraining set Classification results

Line-scan approach

Horizontal deflection of the beam caused the

biggest variation in collected signal therefore

new scan method was proposed, where

horizontal beam scan was replaced by movement

of the painting in front of the Imager performing

vertical line scan.

Noise reduction electronics Basic methodology for noise

reduction is to measure the

outgoing pulse energy (channel

B) and use that to normalise the

returned signal (channel A) from

the target.

Any jitter in the emitted energy

will be eliminated through the

A/B normalisation.

Pulse-by-pulse jitter

Normalised signal