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The relevant information in many vision
applications is encoded into the color of
the scenery. This information in normal
color cameras is extracted based on
the three standard color channels; red,
green and blue (RGB), respectively.
This color reproduction technique
is an approximation and it is often
insufficient to reliably solve a given
machine vision problem. Hyperspectral
imaging overcomes this limitation
by providing a greater number of
spectral bands, while maintaining
an adequate spatial resolution. The
required narrow-band spectral filters
can be implemented at the sensor
level owing to the recent advances in
sensor design. Hyperspectral cameras
equipped with these sensors show
a significantly decreased level of
complexity. This enables compact,
reliable, and easy-to-use hyperspectral
cameras that can benefit virtually any
vision-based solutions in many
applications.
In contrast, hyperspectral imaging, or
imaging spectroscopy, is a combination
of digital imaging and narrow-band
spectroscopy. This technique allows
the light intensity to be captured for
each pixel on the detector for a greater
number of spectral bands (typically
some tens to hundreds). Consequently,
each pixel in the image contains a
full color spectrum (in contrast to
restricting three values for red, green
and blue), which can characterize
the scenery with greater color, detail,
and accuracy. This feature enables
object classification pipelines based
on spectral properties, via statistical
matching or neural networks; thereby
enhancing entirely new approaches in
the vision industry.
The recent progress in sensor design
and processing speed means that a
wide field of applications now can
Imaging with Hyperspectral Sensors: The Optimum
Design for your Application
Frederik Schönebeck, FRAMOUS
application in which accurate color is
key to success.
Color is one of the key parameters
in many vision applications and it is
often used as a basis for classification,
alternate background and foreground
discrimination, or object identification.
Typically, color cameras are equipped
with three broadband color channels,
red, green, and blue (RGB). These
channels are implemented in the form
of a regular, mosaic-like filter pattern;
the so-called Bayer-pattern. With
only these three standard filters, the
resulting color information is merely
approximate and often insufficient to
reliably identify subtle color gradients.
However, robust color discrimination
is often the key to success (e.g., the
discrimination between tissue, nerves
and blood vessels during non-invasive
surgery) and therefore the performance
of conventional color sensors hampers
20 l New-Tech Magazine Europe