22 / 84
benefit from hyperspectral imaging.
The applications include industrial parts
inspection, specimen classification in
medicine and biophysics, and, airborne
remote sensing and military target
detection. This article explains the two
most common operating principles of
hyperspectral cameras, and highlights
the main applications in which they are
typically used.
Principles of
Hyperspectral Cameras
The spectral decomposition of light is
traditionally achieved with a narrow slit
in combination with dispersive optical
elements. This technique allows high
spectral accuracy, but results in an
elaborate and complex optical setup
inside the camera. This can lead to
a large camera footprint, unreliable
performance, and increased costs.
In recent years, the advances in
sensor design have enabled the
implementation of precisely tuned
narrow-band spectral filters at the
pixel-level. While conventional color
sensors have filter patterns with
only three distinct broadband color
channels, hyperspectral sensors have
filter patterns that sample the full
spectral range with a greater number of
evenly distributed narrow band filters.
Depending on the application, this
range can span from the ultra-violet to
the near-infrared spectrum, and might
be significantly beyond the perception
of the human eye. Hyperspectral
cameras can be divided into two main
categories, based on the characteristics
of the filter pattern and the resulting
operating modes: snapshot mosaic
and pushbroom scanning. Both these
modes impose different requirements
on the application setup.
Pushbroom Scanning
The relative motion between the
camera and the imaged object is
often a natural requirement in many
vision applications. Typical examples
include parts inspection on conveyor
belts, remote sensing via aircraft or
satellites, or autonomous agriculture
via unmanned ground vehicles.
These kinds of applications are best
addressed with pushbroom-scanning
hyperspectral cameras, in which
contiguous pixel rows of the image
sensor are coated with spectrally
adjacent narrowband filters. The
relative motion between the camera
and the scenery causes the object
to effectively drift over the image
sensor. By synchronizing the sensor
line read-out to the relative motion
speed, the scenery is imaged line
by line; or, due to the row-wise filter
coating of the sensor, one spectral
band after the other. The full spectrum
is obtained after the object has passed
completely over the sensor. Therefore,
on a two-dimensional area sensor,
the pixel rows image one spatial
dimension, while the columns capture
the spectral dimension. The second
spatial dimension is obtained from the
relative motion of the camera and the
scenery; termed the pushbroom scan.
This working principle is illustrated in
Figure 1.
Typically, the number of spectral bands
Figure 1:
Outline of the CMOSIS CMV2000 sensor with a narrow band filter
pattern aligned with the sensor rows. Each spectral band covers an area given by
the full width (2048 pixels) multiplied by 8 rows. The spectral range from 600nm
to 1000nm is sampled with 100 spectral bands. The modified sensor is provided
by IMEC and is implemented by a number of camera manufacturers.
Image credits:
XIMEA
Figure 2:
Outline of the CMOSIS CMV2000 sensor with repetitive 4×4 pixels
tiles. In each tile, the full spectral range between 465nm and 630nm is sampled
with 16 spectral bands. The modified sensor is provided by IMEC and is
implemented by a number of camera manufacturers.
Image credits:
XIMEA
22 l New-Tech Magazine Europe