CEE 6150: Digital Image Processing
LAB 11: Unsupervised Classification
1
Image
Training
data
ML Class
Single threshold
0.5
ISODATA
10-20 classes
Clump
ML
Classified
Image
ISODATA
Classified
Image
Test data
Confusion
Matrix B
Confusion
Matrix C
The overall task is to perform a classification of a scene using both supervised and unsupervised
methods, evaluate the classifications and compare the results. You will classify the ginna_2000
7-band image using the parameters (and follow on procedures in the case of the ISODATA
classification), and compute the confusion matrices. A flow chart of the processing steps appears
below.
The settings for the 2 procedures are:
# clr
1. Supervised Classification
i. Classes: Select all 8 classes
ii. Prob. threshold: Single Value = 0.5
iii. Data scale factor:
255
iv. Rule images: Not required
2. ISODATA Classification
i. Number of Classes:
ii. Maximum Iterations:
iii. Change threshold:
iv. Min. # pixels in Class:
v. Max. class Stdev:
vi. Min. Class distance:
vii. Max # Merge Pairs:
viii. Max. Stdev.
20-25
30
5
30
4
5
2
6
1
2
3
4
5
6
7
8
CLASS
Bare Soil
Forest
Asphalt
Water
Grass
Orchard
Bldg_dk
Bldg_lt
CEE 6150: Digital Image Processing
LAB 11: Unsupervised Classification
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3. Assign classes to the ISODATA results
There will be between 20-25 classes resulting from the ISODATA classification, each
identified by a number and a color. Your job is to assign the ISODATA classes to one of the
desired classes. You may also identify an ISODATA class as "unclassified" or assign it to a
category not in the classification list. The numbers and colors appear in the chart below:
ISODATA
Class clr
1
2
3
4
5
6
7
8
9
10
11
12
#
Assigned
clr
class
ISODATA
Class clr
13
14
15
16
17
18
19
20
21
22
#
Assigned
clr
class
Some of the classes should be easy to assign. Others will be more troublesome (e.g. all the
vegetation types. For those that appear mixed:
1. create a table listing the ISODATA class and all the possible assignments. (A table
with my assignments is shown below.)
2. Adjust the zoom box to fit within an area that you consider to be a single category
(Orchard and Forest examples are shown below.
3. Display the histogram for the zoom box.
a. Select Enhance > Interactive Stretching
b. In the histogram dialog box, select Source > Zoom
This will allow you to see the relative distribution of ISODATA classes within that
category.
Orchard
Forest
17 19
17
20
20
CEE 6150: Digital Image Processing
LAB 11: Unsupervised Classification
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List of possible assignments (bold indicates the dominant class)
Orig. Class
15
16
17
18
19
20
21
22
Grass
15
Orchard
Forest
17
16
17
18
19
19
20
21
Assigned Class
4
6
6
4
20
21
22
5
5
6
My assignments appear below. Note that there are several classes that do not correspond to any
of the training classes. Class 5 includes asphalt in the parking lots and portions of the shoreline.
In this case I chose to live with the misclassification since there was too much to lose in terms of
the asphalt class. Class 7 part water, part shore, part shadow and part cars. Water may be the
dominant class, but it includes too much misclassification and I chose to leave it unclassified.
Classes appears to correspond to shadows in vegetated areas, and Class 10 corresponds to the
industrial area in the lower right (probably concrete pavement). This I also assigned to "asphalt".
It might be more appropriate now to rename the class "roads". The buildings were all either
confused with other classes or unclassified. My final class assignment was:
ISODATA
Class clr
1
2
3
4
5
6
7
8
9
10
11
12
#
1
1
1
1
2
2
Assigned
Clr
2
2
3
2
clas
water
water
water
water
asphalt
asphalt
unclassified
unclassified
asphalt
asphalt
soil
asphalt
CLASS
ISODATA
Assigned
13
3
soil
14
3
soil
15
4
grass
16
6
forest
17
6
forest
18
4
grass
19
4
grass
20
5
orchard
21
5
orchard
22
4
forest
Your assignment need not match this.
4. Reassign the ISODATA classes to the desired classes.
1. Select Classification > Post Classification > Combine Classes.
2. Select the ISODATA classified image, then click OK. The Combine Classes Parameters
dialog appears.
3. Select a class for input from the Input Classes list. The selected class name appears in the
Input Class field.
4. Select an output class by clicking on a class name in the Output Classes list.
CEE 6150: Digital Image Processing
LAB 11: Unsupervised Classification
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5. When both the input and output classes are selected, click Add Combination to finalize
the selection. The new, combined class to create is shown in the Combined Classes list at
the bottom of the dialog.
To deselect combined classes, select the name in the Combined Classes list.
5. Change the Class colors and names
This is not absolutely necessary, but it will make comparison with the ML classification much
easier.
1. Display the ISODATA classification image.
2. In the ISODATA display menu bar, select Tools > Color Mapping > Class Color
Mapping. The Classification Mapping dialog appears.
3. Select a class name in the Selected Classes list and click on the Color button and select
the new color from the resulting menu. You may also select colors by:
o
Entering new values into the Red, Green, and Blue fields.
o
Move the color adjustment slider bars.
4. Change the class name by editing it in the Class Name field.
6. Select File > Save Changes to retain the new colors.
6. Visually compare the modified ISODATA classification with the ML classification.
7. Create a confusion matrix for the ISODATA classification and compare with the ML
confusion matrix data.
##############################################################################
ML Class. with 0.5 threshold
ISODATA Classification
CEE 6150: Digital Image Processing
LAB 11: Unsupervised Classification
5
1. Evaluation of the classification
a. Visual evaluation
ML Class with 0.5 threshold
The general impression is that the classification is good as far as it goes and that the major
problem is that there are very many unclassified pixels. Unclassified pixels around the
borders are not a surprise; these are mixed pixels and, if the classes on either side of the
border are sufficiently different spectrally, then mixed pixels should not be classified.
Water has been captured quite well with major problems occurring only near the shore where
the water may be turbid or shallow, or where individual pixels combine shore and water.
The orchard in which the training was done has been classified rather effectively, as has the
adjacent orchard. The orchard below the training orchard shows significant misclassification
as forest, while the orchards toward the right side of the image suffer from an excess of
unclassified pixels.
Bare soil has also been recognized most effectively in the field used for training, and is less
well characterized farther away from the training area.
The grass and forest classes are reasonably well characterized without obvious major
problems.
Finally the asphalt class appears to be good as far as it goes, but there are many asphalt pixels
left unclassified. The light-toned buildings in the power plant area have generally been
labeled correctly, but the dark-toned building are largely misclassified or unclassified.
ISODATA classification
The grouped ISODATA classification has done a very nice job with the water in that most of
the lake is properly classified with very few misclassifications offshore and a relatively few
pixels misclassified as asphalt. This classifier has even captured some of the inlet water in the
power plant area.
The asphalt and bare soil classes are rather consistently confused and are also misclassified as
shoreline. The number of unclassified pixels in these classes is not excessive and many could
be recaptured by some contextual operations.
The unsupervised classifier had much more trouble in distinguishing among the vegetated
classes. This is particularly true with the orchard and forest classes.
The unsupervised classification appears to have been incapable of capturing either building
class. In fact, most of the buildings in the power plant area are unclassified.
Discussion
For the ML classification with a 0.5 threshold, the Producer's accuracy is quite high even
though there are a large number of unclassified pixels. The User's accuracy is even higher.
Nonetheless, this is a reasonably accurate representation of the accuracy of the classification.
This is because the areas used for training and testing were a good match. They did not
CEE 6150: Digital Image Processing
LAB 11: Unsupervised Classification
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capture the true range of variability of the classes – thus the large number of unclassified
pixels – but the only misrepresentation is in the extent of the unclassified pixels. 32% of the
image is unclassified, and that is not represented in the confusion matrix or the error metrics.
ML CLASSIFICATION
Confusion Matrices in terms of percentages.
Class
Unclass.
Bare Soil
Forest
Asphalt
Water
Grass
Orchard
Bldg_dr
Bldg_lght
Total
Soil
7.72
91.85
0
0
0
0
0
0
0.43
100
Forest
13.06
0
84.54
0
0
0
2.4
0
0
100
TEST DATA (percentage)
Asphalt Water Grass Orchard
7.7
5.01
2.78
7.54
0
0
0
0
0
0
0
0.66
92.2
0
0
0
0
94.99
0
0
0
0
97.08
4.61
0
0
0.14
87.19
0.1
0
0
0
0
0
0
0
100
100
100
100
Bldg_dk
82.19
0
0
17.81
0
0
0
0
0
100
Bldg_lt
53.32
0
0
0
0
0
0
0
46.68
100
Total
10.36
8.28
9.35
14
32.52
11.12
11.54
0.02
2.81
100
ISODATA CLASSIFICATIOON
TEST DATA (percentage)
Class
Soil
Asphalt
Water
Grass
Orchard
Forest
Unclassified
1.29
4.65
soil
16.9
0.93
asphalt
81.82
water
Bldg-dk
Bldg-lt
Total
0
0
4.19
0
48
0
12.77
0
99.87
8.68
0
35.62
0
7.27
94.21
0
0
0
0
64.38
0.13
22.22
0
0.21
100
0
0
0
0
0
34.27
grass
0
0
0
42.72
47.6
30.91
0
0
14.17
orchard
0
0
0
0
43.65
24.49
0
0
8.32
forest
0
0
0
9.27
4.55
31.83
0
0
5.08
Class 7
0
0
0
0
0
0
0
0
0
Class 8
0
0
0
0
0
0
0
0
0
Total
100
100
100
100
100
100
100
100
100
Suggested improvements ML Classifier: It is not likely that one will be able to extend the
classification to include many of the unclassified pixels and not degrade (much less improve)
the accuracy of the classification. Since there is very little class-to-class misclassification, one
might want to keep the existing classes and define new classes in the unclassified areas that
would extend the classification while giving more control over the class-to-class
misclassification. This would ultimately demand that one define classes for the undefined
classes such as the industrial area.
Classification accuracies are low for the ISODATA classification. There appears to be
significant error for every class but water. The confusion matrix shows the difficulty in
distinguishing among the 3 vegetation classes. There are a number of misclassifications that
are not apparent in the confusion matrix because the problem areas were not covered by the
CEE 6150: Digital Image Processing
LAB 11: Unsupervised Classification
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training and test data sets, e.g., the shoreline and nearshore water being classified as asphalt
and the confusion of asphalt with bare soil. On the other hand after grouping, two new classes
emerged which, by their very presence, avoided some of the errors in the ML Classifier
without a threshold.
Errors and Accuracy metrics
Class
ML 0.5 thresh
Comm.
Omis.
(Percent)
(Percent)
ISODATA
Comm.
Omis.
(Percent)
(Percent)
Bare Soil
Forest
Asphalt
Water
Grass
Orchard
Bldg_dr
Bldg_lght
0
0.91
1.44
0
5.35
2.41
100
1.38
8.15
15.46
7.8
5.01
2.92
12.81
100
53.32
79.04
36.51
0.09
67.3
32.25
31.35
0
0
83.1
5.79
0
57.28
56.35
68.17
100
100
Class
Prod. Acc
(Percent)
User Acc.
(Percent)
Prod. Acc
(Percent)
User Acc.
(Percent)
Bare Soil
Forest
Asphalt
Water
Grass
Orchard
Bldg_dr
Bldg_lght
91.85
84.54
92.2
94.99
97.08
87.19
0
46.68
100
99.09
98.56
100
94.65
97.59
0
98.62
16.9
94.21
100
42.72
43.65
31.83
0
0
20.96
63.49
99.91
32.7
67.75
68.65
0
0
Overall
92.9
Kappa
91.1
Overall
0.864
Kappa
0.827