1. Unsupervised Classification in Imagine
D. Meyer
E. Wood

2. Geospatial data fundamentals
Geospatial information types:
Raster: “images” composed of “pixels”
Vector: points, lines, polygons (“shapes”)
Raster data types:
Continuous
Single attribute (panchormatic = “black & white”)
Multiple attribute (multi-spectral = “color”)
Discrete:
Quantized continuous
Categorical

3. Continuous vs. Categorical
“Feature space” – set of all attributes describing an object.
Student feature space:
Height (continuous)
Weight (continuous)
Hair color (weirdly continuous)
SSN (categorical) -doesn’t make sense to take an “average” SSN
GIS attributes
Continuous – How warm? How bright? How much photosynthesis? What’s the mean population density? Crime rate per 100,000?
Discrete – what type of land cover? In which country is it located?

4. Categorical – Land Cover

5. Classification
Very widely used method of extracting thematic information
Use multispectral (and other) information
Separate different land cover classes based on spectral response, texture, ….
i.e. separability in “feature space”

6. Purpose categorizing data data abstraction / simplification
data interpretation
mapping
for land cover mapping
use land cover class as a surrogate for other information of interest (ie assign relevant information/characteristics to a land cover class)

7. Approaches to Classification
We need some form of automated (rule-based) classification algorithm to allow us to distinguish one surface type from another
Supervised Classification
Unsupervised Classification

8. Supervised classification
Want to separate clusters in feature space
E.g. 2 channels of information
Are all clusters separate?

9. Unsupervised Classification (clustering)
Little input from user required (few assumptions)
BUT means results hard to interpret (may not represent classes we recognize)
cluster pixels in feature space based on some measure of their proximity
interpretation of results / assigned classes
can be useful, e.g. in picking up variations within what would otherwise be distinguished as a single class e.g. stressed/unstressed crop in a single field)
clusters can be of little intrinsic value in themselves
e.g. sunlit trees, shaded trees is perhaps not a useful discrimination if one simply wants to classify 'trees', and so clusters may have to be combined

10. Unsupervised example: 2 classes, 2 bands
DN Ch 1
DN Ch 2
Initial cluster means
Pixel 1 a b
Pixel 2
Assign pixel 1 to cluster a, 2 to b etc.
DN Ch 1
DN Ch 2 b a
Cluster means move towards pixels 1 and 2 respectively
Pixel 1
Pixel 2
DN Ch 1
DN Ch 2
All pixels assigned to a or b - update stats
New positions of cluster means
Split a into 2, recalculate. Repeat….
DN Ch 1
New positions of cluster means
SD of cluster a too large?
DN Ch 2

11. Tools
Identify spectral signatures of different land cover types using tools within Imagine:
Signature editor
Alarm feature
Signature editor statistics
Areas of interest (AOI’s)
AOI tool
Unsupervised classifier (“ISOdata”)
Raster Attribute Editor

12. Open File in Viewer
germtm.img -> RGB -> 5,4,3 (it’s somewhere in Germany)

13. Unsupervised Landsat Classification
In the Imagine toolbar, click on “Classifier”, and select “Unsupervised Classification” from the “Classification” menu.

14. Unsupervised Landsat Classification
In the unsupervised classification (Isodata) window:
Input Raster File: “germtm.img”
Output Cluster Layer (classified image): “xiso.img” (placed in “results” folder)
Output Signature Set: “xiso.sig” (also in results folder)
# of classes is 15, Maximum Iterations is 25, Convergence Threshold 0.950
Under “initialization options:
“Principal Axis”
Std. Deviattions: 2.00
“Close” this window
“OK” in Unsupervised Classification window

15. View classification
Open cluster image “xiso.img” in a viewer (DO NOT CLEAR DISPLAY). Swipe between the two images
Using the inquire cursor, roam around the image and note that all pixel values are between 1 and 15 (max # of classes)
Close Swipe & Cursor windows
Are there any cluster classes you can figure out (for example, what does a cluster value of 4 represent?
Note class names in last column (up to the maximum of “class15”

16. Unsupervised Landsat Classification
Under the “Classifier” menu, click on “Signature editor” (open “xiso.sig”). Plot the signatures.
Select all and plot. Note that the classes have grey scale values.

17. Unsupervised Landsat Classification
Rerun the unsupervised classification again to make a “colorful” classification:
Output Cluster Layer: “xiso_color.img” (stored in the same results folder)
Output Signature Set: “xiso_color.sig” (etc.)
# of classes is 15, maximum Iterations is 25, convergence threshold 0.950
Initialization options: principal axis, std. dev = 2.0
Under “Color Scheme Options”:
Select “approximate true color and select 5,4,3

18. Unsupervised Landsat Classification
Open cluster image “xiso_color.img” in a viewer on top of “germtm.img”. Swipe between the two images, use the inquire cursor as before to roam around.
Note the same 15 classes now have been “colored” based on their appearance as a “5,4,3” r,g,b image.
Does this color scheme help distinguish what the classes are? What is class 4?
Again, note class names in last column (up to the maximum of “class15”

19. Unsupervised Landsat Classification
Open cluster image “xiso_color.img” in a viewer (DO NOT CLEAR DISPLAY). Swipe between the two images
Using the inquire cursor, roam around the image and note that all pixel values are between 0 and 15 (max # of classes)
Close Swipe & Cursor windows
Does this color scheme help distinguish what the classes are? What is class 4?
Again, note class names in last column (up to the maximum of “class15”

20. Unsupervised Landsat Classification
Under the “Classifier” menu, click on “Signature editor” (open “xiso_color.sig”) to bring up the
Select all and plot. Note that the classes are colored similarly to the 5,4,3 RGB display of the input

21. Discussion
Although the classified image “xiso.img’ appears to be in color, it only takes the values of 1-15 (one for each class). These values are assigned a color (like paint by numbers) – this is “psuedocolor”, which is different from the multi-band RGB displays.
What is class 1? The “layers” in the spectral plot are spectral bands (1: blue, 2: green, 3: red, 4: NIR, etc. for all 6 TM bands). What are the classes having high values in layer (band) 4 and low values in layer (band) 3?

22. Unsupervised Landsat Classification
In the Viewer, select Raster -> Attributes. Note the columns are inconveniently organized. We’d like Class Name and Colors to be the first two columns.
Since we’ll be playing around, changing colors to figure out what the classes are, we’d also like a “working” colors column to avoid confusion. That’s why we’ll use the “column” icon to organize this differently. Click on it to bring up the “column properties” window.

23. Assign classes to clusters
In the “column properties” window, note how the entries under the “Columns” section on the left of the window shows how the column headings are organized in the attributes editor. We want to move them around for easier use in sorting out clusters, and make a new working colors column.

24. Assign classes to clusters
Since we want “Class Names” to be on the far left of the attributes editor, we want to move it to the “Top” of the Column Properties list.
Click on “Class Names” in the list, then click on the “Top” button below it
Also change the display width from 100 to 10 (enough characters for our names)

25. Assign classes to clusters
Now we want the “colors” column right next to the “Class Names” column. Select “Color” and move it “Up” just beneath “Class Names”

26. Assign classes to clusters
Now we want to create a working colors column. Press “New” button at the bottom left of the properties window, and in the “Title” window at the upper right, enter “New Color”. Use the “Up” button in the lower left to move “New Color” just below “Color”
Click “Okay” to get back to the attributes editor and note the changes

27. Assign classes to clusters
Back in the “Attributes Editor”, click on the “Color” column heading to select the entire column. Right-click and select “copy”
Now click on the “New Color” heading, right-click and select “paste”
You now have a working color column for changing cluster names to class names and keeping track of your color changes.

28. Assign classes to clusters
As with the previous exercise, select different colors in the attributes editor, then using a combination of swipe, Inquire cursor, and the zoom/pan controls on the Viewer, assign your best guess for classes and assign colors based on the table on the next slide.

29. Assess the results Use the Table below to sort out the classes
Cover type
Color
Comment
Mixed Forest
Green
Lighter green in 5,4,3
Confier
Dark Green
Generally, darker green in 5,4,3
Water
Blue
Watch for turbidity , algae blooms and wind effects
Ag (green)
Yellow
What would this be in Germany?
Ag (bare)
Brown
See open urban – would they get confused?
Dense Urban
Magenta
What is this likely to be confused with?
Open Urban
Pink
What “mixed” signatures comprise this?

30. Assess results Assign all clusters to each of the 15 classes
Go back to the previous table and assess your results.
Save the results