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in pushbroom-scanning hyperspectral
cameras is on the order of one hundred.
This feature yields very detailed
spectral information, which in turn
enables more reliable identification
and classification results. At the same
time, the maximum spatial resolution
is very high, and is determined by the
raw resolution of the sensor along
one dimension (typically 2048 − 4096
pixels), and the scanning speed along
the other dimension. It is important to
note that the high spectral and spatial
resolutions come at the expense of
the scanning requirements, and can
potentially lead to more complicated
application setups. The scanning
requirements, however, are often
intrinsic to the application, and
therefore this is not considered to be a
general disadvantage.
Snapshot Mosaic
Snapshot
mosaic
hyperspectral
cameras are very similar to standard
color cameras. The filter coating is
arranged as a mosaic of repetitive
tiles, but, contrary to the 2 × 2 Bayer
pattern, typically these tiles consist of
4 × 4 or 5 × 5 pixels. The individual
pixels in each of these tiles are coated
with narrowly-defined bandpass filters
(compare with Figure 2) Therefore,
the number of spectral bands is
significantly increased compared to
the traditional red, green, and blue
color channels. It is important to note
that this gain in spectral information
is accompanied with a decrease in
spatial resolution, which results from
the large size of the individual tiles in
the filter mosaic.
Typically, the resulting raw resolutions
are of the order of 500 × 250 pixels,
but can be increased with sophisticated
interpolation algorithms.
The complete spatial and spectral
information can be obtained in one
snapshot, as implied by the name,
and for this reason snapshot mosaic
hyperspectral cameras can be used
for conventional video acquisition, or
other applications where scanning is
not applicable. Consequently, snapshot
mosaic hyperspectral cameras are very
versatile and can be easily integrated
into virtually any application, as a
substitute for conventional color
cameras. These applications include
quality inspection, food sorting, tissue
analysis, endoscopy, and microscopy.
The only drawback of this “ease-of-
use” is the limited number of spectral
bands of approximately 20 compared
to over a 100 with pushbroom-
scanning cameras. However, often this
is still sufficient to address imaging
problems that cannot be solved with
normal color cameras.
Further considerations
Both the sensor designs explained
above do not put any special
constraints on employed lenses, other
than a high transmission and low
chromatic aberration over the spectral
range of interest. Consequently,
cameras with hyperspectral image
sensors can be readily equipped with
existing professional-grade machine
vision lenses.
The output of hyperspectral cameras
comes in the form of 3D data cubes,
with two spatial and one spectral
dimension, i.e., a full spectrum for each
pixel. The concept of this type of data
cube is depicted in Figure 3, where
x and y represent the well-known
spatial dimensions of the image and
the vertically arranged λ1..n represent
the n spectral bands. Note that a
significant amount of image post
processing is required to transform
the raw information into data that
can be further implemented for object
identification or classification.
Conclusion
It is now possible to implement
narrow-band spectral filters at
the pixel-level with semiconductor
thin-film processing. Hyperspectral
cameras using this technology can be
implemented as reliable, compact,
and easy-to-use systems that can
be integrated into many different
applications. These applications can
range from precision agriculture
supported by unmanned vehicles,
to robust discrimination between
tissue, nerves, and blood vessels
during non-invasive surgery. In
addition, this technology can also
significantly improve food sorting or
quality inspection, by providing more
detailed and accurate spectral data
than conventional color sensors.
Specifically, when combined with
powerful computing approaches
like neural networks, capable of
analyzing and extracting the desired
information from vast amounts of
raw data, hyperspectral cameras will
enhance virtually all applications in
which the color of the object plays a
crucial role.
Figure 3:
A 3D data cube, with x and
y representing spatial dimensions,
and λ1..n depicting n spectral bands.
Image credits:
XIMEA
New-Tech Magazine Europe l 23