Lab Instructions
Lab 6, due 7:00 pm October 20, 2008
EES5053/GEO4093: Remote Sensing, UTSA
Student Name: ___________________
Digital Image Processing III: Supervised Classification
Objective: In this lab, you will compute all bands’ radiance, stack
individual bands together, and classify the image (study area) using
unsurprised and supervised classification methods. Finally, you will
calculate the flooded area.
Part I: Concepts and short questions:
1. Summarize the differences between multispectral remote sensing and hyperspectral
remote sensing
2. Using a table to summarize four multispectral remote sensors and four hyperspectral
remote sensors in terms of their resolution and coverage (spatial, spectral, radiometrical,
and temporal), sensor type (whiskbroom, pushbroom, or linear and area array), and their
major applications. (if you cannot get all of those information from the text book and
from the lecture notes, you are encouraging to search those from the Internet).
3. Summarize three EOS satellites (Terra, Aqua, Aura) in terms of the launching time,
major sensors, and purposes.
Part II:
1. Preparation:
(1). Create a Lab6 directory under c:\UserData_ENVI\yourname\. Today we will use
the atmospherically corrected image that you processed in Lab4:
p27r40_July8_2002DOS.img. You do not need to copy this image from Lab4 to
Lab6, but directly open it from Lab4, while save your results to Lab6 directory.
2. Background
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On June 30 - July 8, 2002, a tropic storm hit the central and south-central parts
of Texas. During the 8 days, the storm fell as much as 35 inches of rainfall, with heaviest
depths occurring in the Texas Hill Country northwest of San Antonio. The floods caused
twelve deaths and damage to about 48,000 homes. Nearly 250 flood rescue calls were
reported, more than 130 roads were closed, and thousands of homes and businesses lost
electrical power and telephone service.
3. Calculate the Radiance
In the lab 5, you calculated the radiance and reflectance of band 3 and band 4. In
this lab, you are required to compute the radiance of band 1, band 2, band 5, band 6 and
band 7 using band math tool based on equation (1), table 1, and figure 1 as you did in lab
5. (Caution: for band 6, please use the band 9-high gain for the gain and offset
calculation, i.e. 0.037059*DN+3.200000 for band 6). Output the results to lab6 and
name them as Radiance-b1.img, Radinace-b2.img, Radiance-b5.img, Randiance-b6.img,
Radiance-b7.img, respectively.
L  gain * DN  offset
Table 1
Detector
(1)
gain
offset
Abs Calib?
------------------------------------------------------1
|
15
0.775686
-6.20000
FALSE
2
|
12
0.795686
-6.39999
FALSE
3
|
8
0.619216
-5.00000
FALSE
4
|
7
0.965490
-5.10001
FALSE
5
|
14
0.125725
-0.99999
FALSE
6
|
8
0.066823
0.000000
FALSE
7
|
10
0.043726
-0.35000
FALSE
8
|
27
0.971765
-4.70000
FALSE
9
|
8
0.037059
3.200000
FALSE
Figure 1. Compute Radiance
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4. Stack layers
After you computed the radiance, they are in individual bands. We must stack
them together as one image file for the image classification (in BSQ format for multiple
bands).
From the ENVI main menu, Click Basic Tools->Layer Stacking->Import file,
Select the five radiance bands (make sure that the order is b1, b2, b3, b4, b5, b6,
and b7 from the top to bottom), click ok. (if the radiance b3 and b4 have not
opened yet, you need to open them from your lab5 directory). Keep all the default
parameters the same and Output the result to file in lab6 as
Radn_July8_2002.img. Click ok. All the seven band radiances are stacked
together in the order of b1 to b7.
Later, we will use the radiance to classify the image. We will use both
unsupervised and supervised classification methods to classify the image.
Question 1. do a statistics of all seven band radiances (using the mask.img to exclude
areas outside the image for the statistics), show the results and give a simple explanation
on your understanding of the statistics of each band, relationship between bands, and
relationship between Solar radiation, earth emission and sensor recording of those
radiances.
5. Unsupervised classification
The unsurprised classification method is a method completely based on statistics of pixel
values, to cluster those pixels have the similar statistics (mean, standard deviation, or
others) into one class. In ENVI, we have two types: K-Means and IsoData.
Question 2: Please use lecture notes and ENVI help to summarize the K-Means and
IsoData
In this lab, I want you to use the IsoData method to classify a small subarea. The subarea
is defined by a ROI and can be downloaded from here:
ftp://129.115.25.239/pub/LRSGdata/studyarea.roi
and save it to Lab6; Open this ROI overlaying on your Radn_July8_2002.img.
From ENVI main menu, click Classification -> Unsupervised -> IsoData, a window
called “Classification Input File” popup. You should select the Radn_July8_2002.img.
Click the Spatial Subset button, you will see a window similar as below. You can select
your subset using various methods such as Image, Map, File, ROI/EVF. In this lab, you
will use ROI. Click the ROI/EVF button, and select the downloaded ROI.
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Then the following window will popup. change the parameters to the same as the window
below and save your result to Memory (or your Lab6 directory).
The classified results will appear in the Available Band List. Open the classification
image, copy and paste one to your homework.
6. Supervised Classification
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Training sites
To do a supervised classification, you need to first select training sites. Supposing you
already know many classes in your image, and you need to tell them what they are using the ROI
tool. For example, the ROI window below is what I did for my study:
And the ROIs are on the image below. You can see for each type of ROI, I have marked
multi-places on the image to represent the variation of the same class. You might use different
classes
and
different
areas.
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Maximum Likelihood classification
There are many supervised classification method can be used. In the lab, the Maximum
likelihood method will be used.
Question 3: Please find the exact definition of Maximum likelihood and explain it here.
From the ENVI Main menu, Click Classification->Supervised->Maximum
Likelihood. Then select the same image Radn_July8_2002.img as the input file and
select the same Spatial Subset for classification. Select the six or more classes you trained
from the above step. Input Probability Threshold as 0.1, then output the result to
Memory. Then click Ok.
After it’s done, upload the Class memory to a new window. Then link it with RGB image
and compare your classified image with the RGB742 image. If you are not satisfied with
your classification, you can modify your training site or change your probability
threshold and then re- do the classification until it seems good to you.
Question 4, Find out what the probability threshold means. If using 0.1 as the threshold,
what does this number mean? If you change the number to 0.3, 0.5, 0.9, what does it
happen?
Question 5. Compare the two classified images (supervised and unsupervised) and state
what you find. Do they math well? What are the advantages or disadvantages of the two
methods?
Question 6. Measure and report the flooded area (in km2) for the study area only. You
can use any method you can think about.
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