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erdas>interpreter>radiomatic
enhancement>haze reduction
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Simple corrections
ERDAS
Example
1.) Begin by ope
ning the
file which you just finished georegistering (or
use r:
lawe
rsdatalandsatbbcmy
_
). Display the 432
band
combination.
2.) Select INTERPRETER >
Radiometric Enhancement > Ha
ze
R
eduction option from the main icon bar
me
nu. Note along the
wa
y all
the other
options. The HAZE
REDUCTION dialog will
appear.
This
dia
log offers you a chance to see the
will preform a
t
ransformation on the d
ata.
Click on the VIEW button t
o
start the ER
DAS Model build. The DeHaze
model will be loaded.
This model
is very simple.
It illustrates the step
s that ERDAS
will take. The Input Raster is your image, the PSF
Kernel is a convolution filter. To understand how
one pixel is
convolved, imagine that
the con
volution ke
rnel is
overlaid on the data file values of the image
(in one band
), so that the
pi
xel to be con
volved is in
the center of the
window.
The output value for the
convolution is, each value in the
ke
rnel is multiplied by the image pixel
value that corresponds to
it. The
products are summed, and th
e total is
divided by the sum of th
e
values in the
kernel and assigned back
to the cente
r pixel. The kernel is then
moved
to the ne
xt
pixel.
A High-
Fre
quency
kernel
(high pass) has the effect of
increasing spatial frequency. That is to say, when
this kern
el is used on a se
t
of pexels in which a relatively low
value
is surrounded by
highe
r values the low
value
gets lower. Whe
n used with pixels in
which a relatively high
value is
surrounded b
y lower
value
s the high value
becomes
higher. In this way a simple
spatial frequency of ha
ze
ma
y be
reduced.
3.) Now fill out the input and output
fields of the Ha
ze R
eduction
dialog. The software now
knows that the
data is Landsat T
M data and
will now pick a
aze
model for
Landsat. Click on VIEW to see it.
The grahic model
illu
strates the steps that ERDAS will
go through. This
you can
see
it consist of picking between a matrix of
weights for either landsat 4 or 5
d
ata and the the
n
determining the majo
rity
value with a ke
rnel, put
that dat
a out to memo
ry
and the dete
rmining if any
pixel has a
val
ue of
zero be
fore
ma
king the output
file. I
wouldn't try to figure
this one out. It
is but one of the
various
approaches.
Fill out the
dialog for input and output and click OK. Display
your
results side by side
and look at the spatial profiles of the
refle
ctence values
I also did a Point_Spread
model (not using the model for Landsat
Data). Lo
ok at
t
he results.