The
advances
in
remote
sensing
and
geographic
information
that
made
the
way
for
the
development
of
Hyperspectral
remote
Sensors.
Imagery
Spectroscopy
is
also
known
as
Hyperspectral
Remote
Sensing
and
it
is
a
new
technology
that
is
presently
being
investigated
by
scientists
and
researchers
and
it
would
deal
with
the
detection
and
identification
of
minerals,
vegetation,
man-made
materials
and
backgrounds.
It
combines
imaging
and
spectroscopy
in
one
system
and
it
often
includes
large
data
sets
and
require
new
processing
methods.
Like
other
spectral
imaging,
Hyper
Spectral
imaging
collects
and
processes
information
from
across
the
electromagnetic
spectrum.
Unlike
the
human
eye
that
can
see
the
visible
light
in
three
bands(red,
blue
&
green),
spectral
imaging
divides
the
spectrum
into
more
bands.
The
technique
of
diving
images
into
bands
that
can
be
extended
beyond
the
visible
region.
Hyperspectral
Remote
Sensing
makes
use
of
Hyperspectral
Sensors.
The
sensors
collect
information
as
a
set
of
images,
where
each
image
represents
a
range
of
electromagnetic
spectrum
and
is
also
called
spectral
band.
All
of
these
images
are
further
collected
and
integrated
to
form
a
three-dimensional
hyperspectral
cube
for
the
purpose
of
processing
and
analysis.
The
precision
of
these
sensors
is
typically
measured
in
spectral
resolution,
which
is
the
width
of
each
band
of
the
spectrum
that
has
been
captured.
It
is
possible
that
the
senors
might
detect
a
large
number
of
fairy
narrow
frequency
bands
giving
the
possibility
to
identify
objects
even
if
they
are
captured
in
a
handful
of
pixels.
Spatial
Resolution
is
a
factor
in
addition
to
Spectral
Resolution.
If
the
pixels
are
sufficiently
large
then
multiple
objects
are
captured
in
the
same
pixel
and
it
becomes
difficult
to
identify
each
of
them.
Also
if
the
pixels
are
too
small,
then
the
energy
that
is
captured
in
each
sensor
cell
is
quite
low
and
the
decreased
signal-to-noise
ratio
reduces
the
reliability
of
measured
features.
The
acquisition
and
refining
of
hyperspectral
images
is
also
referred
to
as
imaging
spectroscopy.
Applications:
There
are
a
few
scenarios
that
make
use
of
Hyperspectral
Remote
Sensing.
- Biomass burning: subpixel temperatures, smoke
- Atmosphere: cloud properties, water vapor, aerosols
- Snow/Ice: snow cover fraction, melting
- Geology: minerals and soil type
- Commercial: agriculture, forest production and mineral exploration
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