1. ALI assignment – see amended instructions
Webpage – see also article and website outlines
TIF 1 2 3 4 5 6 7 8 9 10

2. Classification review
Unsupervised classification:
clustering into classes
identification of classes
Supervised classification:
training areas to ‘train’ the classification,
check the statistics of the classes created
check resulting coverage for errors

3. Classification display
Classes are numbered 1 to 'n', so the channel 'DNs' are low. Colours are assigned arbitrarily (can be changed by editing the PCT)

4. Unsupervised – how it works
Algorithm starts with statistical seed points
Assigns each pixel to the closest seed
Calculates group mean
Re-assigns pixels to the closest group mean
Re-calculates group mean
Iterates (10?) until relatively little change and fixes groupings

5. Iterations in unsupervised classification
Final step .. Assigning names to clusters (and merge some)

6. Unsupervised classification
Input bands selected – minimum 3 or 4; more acceptable

7. Post-classification steps
Checking the display
Merging and adding classes
Sieving (and filtering)
Accuracy assessment
Conversion of results to vectors – GIS layers
Applying results to other areas ?
Advanced classification options

8. Merging and adding classes
Classes can be merged if they overlap spatially or are not distinguishable spectrally.
Merging
Unsupervised – clusters are not really separate features
Supervised – when its not possible to separate two types
Splitting / adding
Unsupervised: one cluster covers too much area – run again with more clusters
Can generate many clusters, and then group merge
Supervised:
Class covers too much area – create new training class or delete training areas
Areas unclassed – create new training class

9. Sieving (and filtering)
Classification ALWAYS produces a 'salt and pepper' effect with isolated pixels
This is a result of a. the local variations in DNs and b. using ‘per-pixel’ classifiers

10. Mt. Edziza – classification and sieve (150 hectares minimum)
   -  recognises connectivity of adjacent pixels in the same class
   - special classes e.g. wetlands can be specified and preserved
- removes small sub-areas; does not ‘blur’ edges like filtering

11. Accuracy assessment
This requires knowing what is reality at some pixels (ground truthing), and how they were classified. This generates a ‘confusion matrix’
The diagonal represents pixels correctly classified
An off diagonal column element = an error of omission
An off diagonal row element = error of commission

12. Supervised classification methods a. Minimum distance b
Supervised classification methods a. Minimum distance b. Parallelepiped c. Maximum likelihood

13. Calculation of mapping accuracies

14. Measuring accuracy
The overall yardstick of  85% accuracy is held up as a (rarely achieved) ideal.
User's accuracy: the % of a type of pixels that are correctly classified
Producer's accuracy: the % within a classified class that are really that type
Kappa coefficient provides a more unbiased estimate of overall agreement κ
Interpretation
< 0
No agreement
0.0 — 0.20
Slight agreement
0.21 — 0.40
Fair agreement
0.41 — 0.60
Moderate agreement
0.61 — 0.80
Substantial agreement
0.81 — 1.00
Almost perfect agreement

15. Vector conversion
After classes are finalised, the adjacent same class pixels can be transformed into vector polygons, using polygon growing or raster to vector options (see feature extraction and lab 7)

16. Applying classification results to other areas (or other dates)
spectral signatures are not usually transferable due to:
environmental, local, atmospheric, and topographic variables
- also temporal variables - time of year and advance of the seasons

17. Input channels for classification
Input channels for classification .. not just bands how many and which ones?
Landsat TM bands
Channels created from Bands
Ratios
Indices
Greenness
DEMs
Use of masks

18. Advanced classification algorithms
These incorporate other interpretation elements beyond DN alone (‘colour/tone’):
-  Texture (example - Ruiz et al, 2004) PCI - TEX
 - Contextual classifiers PCI - CONTEXT context table
 - fuzzy classifiers PCI - FUZCLUS
Object oriented programs (eCognition) Project example
Neural Networks  PCI: NNCLASS

19. Classification summary
There are many articles on classification approaches:
Input channel combinations
Best algorithms
New approaches
There is no single best solution