Multivariate analysis of hyperspectral images...Unscrambler HSI •Software for Multivariate Data...
Transcript of Multivariate analysis of hyperspectral images...Unscrambler HSI •Software for Multivariate Data...
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
Marketing Manager Morten Hansen
Geir Rune Flåten