CEE 615: Digital Image Processing
Lab 12; Problem 7: Classification & Error Analysis
1
Due: 24 April
This is a combined lab and problem set. Save the output (the final version of the ISODATA
classification and the three confusion matrices) for submission.
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
6-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.
Image
ML Class
No threshold
Training
data
Classified
Image A
ML Class
Single threshold
0.5
ISODATA
10-20 classes
Classified
Image B
Test data
Confusion
Matrix A
Confusion
Matrix B
Clump
Classified
Image C
Confusion
Matrix C
1. Supervised Classification
a. Prob. threshold:
None
b. Data scale factor:
255
c. Rule images:
Not required
2. Supervised Classification
a. Prob. threshold:
Single = 0.5
b. Data scale factor:
255
c. Rule images:
Not required
CEE 615: Digital Image Processing
Lab 12; Problem 7: Classification & Error Analysis
3. ISODATA Classification
a. Perform the 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.
2
Due: 24 April
20-25
30
5
30
4
5
2
6
b. Reassign classes
Using the training classes as a guide, use the Combine Classes function to group the
ISODATA Classes to represent the 6 desired classes.
i. Display the original image and overlay the training ROIs
ii. Link the two images
iii. Display the Cursor Location/Value utility
iv. Examine each of the training ROIs and determine which ISODATA classes
contribute to the classes of interest. Write these down.
v. Eliminate duplicates. (No ISODATA class should be assigned to more than one
training class.)
vi. Select Classification > Post-classification > combine classes
Combine Classes to selectively combine classes in classified images.
Note 1: Combining classes or removing the unclassified class effectively deletes
those individual classes.
Note 2: It is best if the output class is one of the input classes, e.g., if classes 1, 2,
3 & 4 are all water classes, collapsing them into one class by reassigning
classes 2, 3, & 4 to class one will avoid ambiguity.
Save the combined class version classification image and save your notes about
which classes were combined and which classes (e.g., water, bare soil, …) they
are intended to represent.
4. Evaluate the classifications.
a. Visual Analysis – Compare the three classified images visually.
- Use the training and test ROIs to evaluate how well the classifications have
performed. Were the pixels in the test areas classified correctly? Are there many
unclassified pixels in these areas?
- Examine areas that belong to classes not included in the classification, e.g., cars in the
parking lots, buildings in the power plant complex, shoreline, roofs of houses (just
below the parking lot toward the center of the image).
- If there are misclassifications, are they reasonable, e.g., there are bare soil or grass
patches in the orchards. This may be a correct classification in terms of the land
cover, but it is an incorrect classification in terms of land use.
CEE 615: Digital Image Processing
Lab 12; Problem 7: Classification & Error Analysis
3
Due: 24 April
- Are there any problems that could potentially be corrected by adjusting the
classification procedure (ROI adjustment, increasing or decreasing the number of
classes, changing classification parameters, …..)
b. Confusion Matrix - Create the confusion matrices and compute the classification
accuracy estimates using the test ROIs provided for each of the three classified images.
Save the confusion matrix. Please format the matrix as a table.
Problem Set 7
1. Submit
i. the ISODATA classification
ii. 3 confusion matrices, one for each classification
2. Make recommendations for improving the classification.
Based on your visual analysis and a review of the confusion matrices, make a
recommendation for improving the classification. That is, consider where there are
problems with omission or commission and come up with a strategy for adjusting the
training data or the classification parameters that would be likely to result in an
improved classification. Alternatively, justify why the classification is not likely to
improve with adjustments.
3. Develop your own accuracy estimate.
Develop a simple accuracy estimate for the user who is interested only in the area
(number of pixels) covered by each class, regardless of absolute accuracy. For this
purpose errors of omission and commission are acceptable as long as they balance out
for each class. Specify the value of your measure for a) "perfect" accuracy, b) omission
> commission, and c) commission > omission. Also note whether the scale is linear or
nonlinear and whether the more sensitive to an excess of one type of error or another.