Multivariate analysis

of hyperspectral images

Geir Rune Flåten

Introducing Unscrambler HSI:

Imaging

What is hyperspectral imaging (HSI)?

Pixels

Wavelengths

K

J

I

From: https://www.osa-opn.org/home/articles/volume_26/october_2015/features/

hyperspectral_imaging_for_safety_and_security/

• Each pixel is represented not by one gray

value, but by a vector of gray values: spectra

• A stack of gray level images, one for each

variable (wavelength): data cube

Spectroscopy

+

Unscrambler HSI

• Software for Multivariate Data Analysis (MVA) of

Hyperspectral Images

• First version: Tailored for classification applications:

− Explorative analysis of HSI data in the spectral domain

− Calibration model development

• Much more output produced from a single model

compared to generic image analysis tools

Additional output compared to

Generic Image Analysis Tools

• Interactive & informative plots: model interpretation and

mapping back to image data

• Spectral transformations: separate chemical absorbance

from physical effects

• Flexible model validation options

• Variable selection: Significance tests and

remove/downweight irrelevant spectral regions

• Statistical tests for detection of outliers

History:

Origins in remote sensing

• 1972: Launch of LANDSAT-1 satellite

• Objective: study/monitor Earth’s

landmasses

• 2 instruments:

Camera system

Multispectral scanner (MSS)

−4 spectral bands:

green, red, 2 infrared

From: https://landsat.gsfc.nasa.gov/landsat-1/

History:

Development driven by Earth remote sensing

1994

Late

1980s

1974

First portable field

reflectance

spectrometer (PFRS,

0.4-2.5 µm)

Several

commercial

hyperspectral

imagers in the

market

First spectral

cube &

Spectral Image

Processing

System (SIPS)

developed:

software

supported in all

computing

platforms

Early

1990s

SIPS evolves to ENVI

(Environment for

Visualizing Images)

(beginner-friendly)

Wider research

community emerges,

focus on terrestrial

surface mapping

Development of field

spectrometer

applicable to many

research fields MacDonald et al. Remote Sens. Environ. 113 (2009) S2–S4

History: Factors behind HSI growth

• Technological advancement in instrumentation: new sensor

types & sensor arrays

• Improved data storage capabilities

• Higher speed for software handling large data files

HSI Applications

• Precision agriculture: Robot or drone diagnosing

individual plants for stress induced by disease, insects,

water or nutrient deficiency, etc.

• Food: Characterization of salmon filets for grade sorting

Aspirin

Paracetamol

From: He et al. Innov. Food Sci.

Emrg. Technol. 18 (2013)

237-245

• Pharma: In-line monitoring of active ingredient in tablets

at production line From: J. Colling, Stellenbosch

University, South Africa

HSI Applications

• Recycling: Sorting of plastic waste for efficient waste

management

From: Grahn et al. Encyclopedia of

Analytical Chemistry. John

Wiley & Sons.

• Energy: Pipeline monitoring for condition assessment and

damage or leak detection

HSI Applications

• Manufacturing: Real time detection of defects in silicon

wafers and solar cells

From: Turek et al. Energy

Procedia. 92 (2016)

232-235

Defect 1

Defect 2

• Environmental: Remote sensing for monitoring

lake water quality

From: https://aviris.jpl.nasa.gov

/data/free_data

Demo

text

Demo Dataset

• text

From: J. Colling, Stellenbosch

University, South Africa

Thank you!

More info:

https://www.camo.com/unscramblerhsi/

Product Manager Lars Gidskehaug

lg@camo.no

Marketing Manager Morten Hansen

Morten.Hansen@camo.no

Geir Rune Flåten

grf@camo.no