1. Image Classification
FE423 - March 2, 2000

2. Labs Why use Arc commands? Use Grid functions polygrid cov grid … 30 y
grid = Polygrid(cov …)
Stackscattergram <grid> # autoscale

3. Outline What Regression is About What Classification is About
Looking at Pack
Landsat TM Bands
Image Classification
Classification Probability

4. Regression Is Not About:
Model Form
y=a0+a1x1+a2x2
y=a0x1a1x2a2
Parameters a0 a1 a2
Does y depend on x2? #1, #2
Does x1 confound x2? #2

5. Data Regression Is About:
You need to compare (cost, sediment, etc) from alternate management plans,
but you don’t have a predictive model.
You imagine the form (i.e. y=a0x1a1x2a2) of a model but not the parameters
and past measurements of outputs.
How do you use them to infer values for the model parameters, so you can compare your management plans?

6. Problem: Image Classification
You need to analyze entire watershed
But your stand, road, etc data is for only some ownerships
You do have landsat images for the entire basin
But how do you use them to make grids of stands and road?

7. Looking at Pack Forest
Ortho-photo

8. Looking at Pack Forest
Landsat Thematic Mapper 1-2-3

9. Looking at Pack Forest
Landsat Thematic Mapper 1-4-7

10. Landsat TM Bands 1. blue dirt 2. green 3. red 4. near-IR leaves 5. -
5. -
6. -
7. thermal IR heat

11. Landsat TM Bands
Stackhistogram nwgrd

12. Landsat TM Scattergram
Using bands 1, 4, and 7

13. Landsat TM Scattergram
Similar sites cluster together

14. The Idea Behind Classification
Identify some training cells
Make normal distributions that ‘describe’ these cells
Use these distributions to classify all the other cells

15. Image Classification in GRID
Make classes
class1 = con(road, 1, lake, 2, age < 20,3,…)
CLASSSAMPLE <stack> <sample_ grid>
Make signature file
sigfile = CLASSSIG(<stack>, <sample_grid>)
isosig = ISOCLUSTER(<stack>, <nclass>)
Classify the image
MLCLASSIFY(<stack>, <signature>)

16. Types of Image Classification
Supervised Classification
You tell Grid what you are looking for.
-Training sites
-Existing coverages
Unsupervised Classification
Grid decides what it can find best.

17. Supervised Classification
Using existing coverages to create the training set
class = con(road,1,lake,2,age < 20,3,…)

18. Supervised Classification
class = con(road,1,lake,2,age < 20,3,…)
Can landsat really distinguish canopy structure?
soil green dark green

19. Supervised Classification
Training Sites
CLASSSAMPLE <stack> <sample_ grid>

20. Comparing Signature Files
Drawsig sigfile all
Training areas
coverages

21. Unsupervised Classification
Distribute class means
Class data points according to the nearest means
Take mean of each class
Repeat
2&3 a lot

22. Unsupervised Classification
isosig = ISOCLUSTER(<stack>,<nclass>)
Resulting clusters
Stack values

23. How to Classify a Cell
preclass = mlclassify(<stack>,<signature>)

24. Comparing Classifications
The overlap of the signature file varies with the fitting method and information that went into it.
Which will predict land covers better?
covers
trained
unsupervised

25. Which predicts better?
original
covers
Unsup-ervised
trained

26. Probability of Classification
Classification - We can classify a cell according to which class gives a higher likelihood.
Probability - the probability that the cell is actually in that class.

27. Classification Probability
1. Probability that the chosen class is correct.
MLCLASSIFY(<stack>,<signature>,{reject_fraction},{EQUAL|SAMPLES|FILE},{a_priori_file},{o_reject_grid})
Training set
Covers

28. Classification Probability
2. Probability that any given class is correct.
Prob = CLASSPROB(<stack>, <signature>)
lakes
roads

29. Review: Image Classification
You need to analyze entire watershed,
but your stand, road, etc data is for only some ownerships.
You do have landsat images for the entire basin.
So, create a training set,
calculate the distributions, and
classify the rest of the watershed.

30. Review: Classification in GRID
Make classes
class1 = con(road, 1, lake, 2, age < 20,3,…)
CLASSSAMPLE <stack> <sample_ grid>
Make signature file
sigfile = CLASSSIG(<stack>, <sample_grid>)
isosig = ISOCLUSTER(<stack>, <nclass>)
Classify the image
MLCLASSIFY(<stack>, <signature>)