Hyperspectral Imaging Technologies and Applications 08. Nov. 2016 Gion-Pitschen Gross
Agenda 1. Spectral Imaging Basics 2. Benefits of Spectral Imaging 3. Data Acquisition 4. Application Example 5. Other Applications 6. Desired Camera Characteristics
Spectral Imaging Basics
The Electromagnetic Spectrum source: http://en.wikipedia.org/wiki/file:em_spectrum.svg author: Philip Ronan
The Visible Spectrum The World in Grey Monochrome
normalized absorbance The Visible Spectrum Human Vision Human cone sensitivity Wavelength [nm] author: Vanessaezekowitz at en.wikipedia
Spectral Imaging Basics - Overview Monochrome RGB Multispectral Hyperspectral Spectroscopy Spatial Information Band Numbers Spectral Information yes yes yes yes no 1 3 2-10 >10 continuous No limited Yes Yes Yes
Benefits of Spectral Imaging
Benefits of Spectral Imaging monochrome R G B more information
Benefits of Spectral Imaging The more spectral data, the more information Differentiation of materials Identification of material characteristics Determination of water content Material composition
Data Acquisition
Hyperspectral Imaging Data Acquisition Whiskbroom Pushbroom Staring Snapshot Source: Li Q et al., Biomed. Opt. 18(10), 2013
Application Example
Application Example 3 White Powders Source: Optotechnik und Bildverarbeitung (OBV) Christian Günther Frank Friehl Prof. Dr. Heckenkamp
Remission [%] Application Example 3 White Powders 110 Use Spectral Information Sodium Carbonate 100 90 80 70 60 50 40 30 20 Sodium Carbonate Source: Optotechnik und Bildverarbeitung (OBV) Christian Günther Frank Friehl Prof. Dr. Heckenkamp 10 0 350 500 650 800 950 1100 1250 1400 1550 1700 Wavelength [nm]
Remission [%] Application Example 3 White Powders 110 Use Spectral Information Natron Sodium Carbonate 100 90 80 70 60 50 40 30 20 Sodium Carbonate Natron Source: Optotechnik und Bildverarbeitung (OBV) Christian Günther Frank Friehl Prof. Dr. Heckenkamp 10 0 350 500 650 800 950 1100 1250 1400 1550 1700 Wavelength [nm]
Remission [%] Application Example 3 White Powders 110 Use Spectral Information 100 Powdered Sugar Sodium Carbonate 90 80 70 60 50 Sodium Carbonate Powdered Sugar Natron 40 30 Natron 20 Source: Optotechnik und Bildverarbeitung (OBV) Christian Günther Frank Friehl Prof. Dr. Heckenkamp 10 0 350 500 650 800 950 1100 1250 1400 1550 1700 Wavelength [nm]
Remission [%] Application Example 3 White Powders Monochrome Monochrome 110 100 Powdered Sugar Natron Sodium Carbonate X 90 80 70 60 50 40 30 Sodium Carbonate Powdered Sugar Natron 20 Source: Optotechnik und Bildverarbeitung (OBV) Christian Günther Frank Friehl Prof. Dr. Heckenkamp 10 0 350 500 650 800 950 1100 1250 1400 1550 1700 Wavelength [nm]
Remission [%] Application Example 3 White Powders Color (RGB) Color (RGB) 110 100 Powdered Sugar Natron Sodium Carbonate X 90 80 70 60 50 40 30 Sodium Carbonate Powdered Sugar Natron 20 Source: Optotechnik und Bildverarbeitung (OBV) Christian Günther Frank Friehl Prof. Dr. Heckenkamp 10 0 350 500 650 800 950 1100 1250 1400 1550 1700 Wavelength [nm]
Remission [%] Application Example 3 White Powders Consider Infrared 110 100 Powdered Sugar Sodium Carbonate 90 80 70 60 50 Sodium Carbonate Powdered Sugar Natron 40 30 Natron 20 Source: Optotechnik und Bildverarbeitung (OBV) Christian Günther Frank Friehl Prof. Dr. Heckenkamp 10 0 350 500 650 800 950 1100 1250 1400 1550 1700 Wavelength [nm]
Remission [%] Application Example 3 White Powders SWIR SWIR 110 100 Powdered Sugar Natron Sodium Carbonate X 90 80 70 60 50 40 30 Sodium Carbonate Powdered Sugar Natron 20 Source: Optotechnik und Bildverarbeitung (OBV) Christian Günther Frank Friehl Prof. Dr. Heckenkamp 10 0 350 500 650 800 950 1100 1250 1400 1550 1700 Wavelength [nm]
Remission [%] Application Example 3 White Powders SWIR 110 100 Choose bandpass filter SWIR Powdered Sugar Sodium Carbonate 90 80 70 60 Natron 50 40 30 20 Source: Optotechnik und Bildverarbeitung (OBV) Christian Günther Frank Friehl Prof. Dr. Heckenkamp 10 0 350 500 650 800 950 1100 1250 1400 1550 1700 Wavelength [nm]
Sometimes a filter is all it takes Depending on task at hand, one filter can be enough Can be a small and robust solution for easy integration A hyperspectral solution reduced to one relevant bandpass
Spectral Remission [%] And sometimes a multi / hyperspectral solution is required Identification of recyclable plastics Separation of Recyclable Plastics Wavelength [nm] Source: Optotechnik und Bildverarbeitung (OBV) Frank Friehl Prof. Dr. Heckenkamp
Other Applications
Other Applications Food Sorting Recycling Agriculture / Aerial Sensing Farming Detection ripeness Detection of impurities, foulness Separation of plastics Waste separation Separation of building material Detection of moisture content Detection of nutrients Mineralogy Reconnaissance Space Exploration
Desired Camera Characteristics
Desired Camera Characteristics High frame rates High spectral sensitivity Low noise (read-out noise) High linearity stabilized operating conditions (TE cooling)
Thank you! Visit us at booth F62 Gion-Pitschen Gross Product Manager Gion-Pitschen.Gross@alliedvision.com Allied Vision Technologies GmbH Klaus-Groth-Strasse 1 22926 Ahrensburg Germany www.alliedvision.com