Digital Image Processing I: Atmospheric Correction, Radiance, Reflectance, NDVI from Landsat Image

breezebongAI and Robotics

Nov 6, 2013 (4 years and 2 days ago)

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1

Lab Instructions

Lab
3
, d
ue
5
:
3
0
pm

September
2
5
, 200
7

E
E
S5053
/ES4093
: Remote Sensing
,

UTSA

Student Name
: ___________________


Digital Image Processing I:
Atmospheric Correction, Radiance,
Reflectance,

NDVI from Landsat Image


Objective:


In this lab, you
will learn
the basic
procedure of
digital
image
processing from
creating
Region of Interest (ROI), Cloud Mask,
A
tmospheric correction

(DOS
)

Part I: Concepts

and short questions
:


Refraction
:


Atmospheric scattering:


Absorption and major atmospheric window
s for remote sensing on Earth surface:


Reflectance
, spectral reflectance, albedo


Explain why we see blue sky and why we see orange and red

sky

in sunset


Solid angle and radiance


Explain why we need to
do
atmospheric correction in remote sensing


Part I
I


1
. Preparation:


(1).
Copy the data directory
Lab
3

in the server
(
\
\
129.115.25.240
\
XIE_misc
\
Fall2007
-
RS
\

) into c:
\
Fall2007
-
RS
\
YourName
\
. Please
always remember to output your results to your
lab directory (i.e. Lab3 for today’s
lab) not to the default ENVI directory.


(
there is another way to get the lab data from the server without physically copy the
data to your local computer. If you want to know how to do that, please ask me
).



2


(
2
). Tod
ay we will use
a new

image

(NE_p27r40_020708_12347)
.

Open

this image
as R(b7), G(b4), B(b2
). This is the image
acquired
on July 8, 2002, just after the
big
flood
event
of 2002. In the
succeeding

lab
s
, you will
classify and
compute the
flooding areas.



2.
Atmospheric Correction

In the lectures, you learn a lot about the interactions between atmosphere and EMR.
And
you know that the remotely sensed
radiance includes two parts: one from the target area
(which is what we want), the other one
is
from the path (
path radiance, which is what we
do not want).
The process to remove the path radiance is called atmospheric
correction
. There are two types of atmospheric corrections: (1) absolute atmospheric
correction: radiative transfer
-
based atmospheric correction and

empirical line calibration
and (2) relative radiometric correction: Dark Object Subtraction (DOS) and multiple
-
data
image normalization using reg
ression. In this lab
, we will do a simple DOS correction.

The principals of DOS includes (1) find
the darkest

object
in the image;

(2) assume that
its
spectral reflectance
should be all zero

(target radiance);

(3)
the measured values above
zero are

assumed to be the atmospher
ic

noise (
or path radiance) and uniformly distributed
on the image area;
(4) subtract the

path radiance from each pixel
radiance
of the image
,
then we should get a relatively atmospheric free image
. Usually these dark objects are
water bodies (see below figure 1, the fresh water has very low

reflectance, meaning water
absorbing most of light,
especially after 0.75 µm).



Figure 1
. Spectrum signature of different objects


3

In ENVI, the DOS

is called Dark Subtract, click
Basic Tool
-
> Preprocessing
-
>
General Purpose Utilities
-
> Dark Subtract
-
>

select the
NE_p27r40_020708_12347
.tif

image
as the
input file, click
OK
, select the
Band Minimum
, which means that the minimum value of
each band will be automatically selected, and then this value will be subtracted from all pixels in
this band. Output the new image to Memory or
your

lab
directory and
nam
e it

as
DOS
_
020708.tif
. In this image, the darkest objects should be the water bodies in lakes in the southern
San Antonio.

After this correction, the resulting image is ready for the following steps.

Question

1
:

Compare change of the statistic results b
efore DOS

(
NE_p27r40_020708_12347
.tif)

and after DOS

(
DOS
_020708.tif
).



3
.
ROI
tool


When you upload this image

(DOS
_020708.tif)

as R(b7), G(b4), B(b2), you will see
there is lots of cloud

(white or dark pixels)
. For these pixels, we
must

mask them as n
o
data.


Click
Basic tools
-
>Region of Interest (ROI)
-
>ROI Tool
, then a ROI window popup as
figure 2, check
Image

window as the ROI map window. Click
ROI Type
, check
Polygon
, which means your ROI shape is a or many polygons. Move you mouse over the
Cloud a
rea, then press down the
left key

of your mouse, draw a polygon around the
cloud, then
double right click

your
mouse, the polygon will be filled. After you mask all
of the cloud pixels,
click
File
-
> Save ROIs
, select your ROI
output directory
and save as
C
loud
_020708
.roi
.





Figure

2. The ROI tool



4

3. Cloud Mask


We will use ROI

in step2

to build our cloud mask.

Click
Basic tools
-
> Mask
-
>Build
Mask, click Option
-
>import from ROI

(Note: check Selected Area Off)
,

and select
the
Cloud
_020708.roi

as the input

file, then output as

mask_band
.


Click
Basic tools
-
> Mask
-
>
Apply

Mask
, select
DOS
_020708
.tif

as the input file, select
mask_band

as the mask band, Output to file:
Mask
_DOS
_020708.tif
.


Question 2,

C
ompare the
statistic
results from ques
tion 1 with statist
ics after Cloud mask.