1. Supervised Classification
Mirza Muhammad Waqar
Contact:
EXT:2257
RG610
Course: Introduction to RS & DIP

2. Contents Hard vs Soft Classification Supervised Classification
Training Stage
Field Truthing
Inter class vs Intra Class Variability
Classification Stage
Minimum Distance to Mean Classifier
Parallelepiped Classifier
Maximum Likelihood Classifier
Output Stage
Supervised vs Unsupervised Classification
These are the contents of my presentation.

3. Hard vs Soft Classification
Hard Classification
In hard classification, we can assign mixed pixels are pure pixels. It means we create an additive error in our pure class.
Soft Classification
In soft classification, for mix pixels, we identify the dominance and co-dominance factors in pixel. Through this analysis we can identify at the most three classes in one pixel. Though this analysis we can’t identify a class that is contributing less than 20% in the pixel.

4. Supervised Classification
Such Classification, in which human interruption involve.
Totally human decision dependent.
Analyst define training sites, and on the base of these training sites, clusters formed.

5. Supervised Classification
There are three phase in supervised classification.
Training stage
Classification stage
Output stage

6. Training Stage Clear objective of classification
Experiment on the image for understanding different land covers exit in the image.
Identify the major variations in the image (hot spots).
Any spectral variation that is new for analyst.
Create multiple false color composites of ground truthing area.
Ground truthing for hot spots identification.

7. Field Truthing Alternate for not accessible hot spots Historical data
Local person’s knowledge
High resolution imagery

8. Inter-Class Variability vs Intra-Class Variability
It means variability among different classes in satellite image.
Separating different land cover classes in satellite image.
Accuracy of classification is dependent on inter- class variability/separability.

9. Inter Class Variability

10. Intra-Class Variability
Within class variability.
Used to map sub types of land covers, e.g. forest, bare soil, rocks etc.
Feature space is a useful tool for within-class variability but the prediction through feature space is totally dependent on spectral signature.
An appropriate feature space should be choose for intra-class variability.

11. Classification Stage There are three classifier.
Minimum Distance to Mean Classifier
Parallelepiped Classifier
Maximum Likelihood Classifier

12. Minimum Distance to Mean Classifier

13. Parallelepiped Classifier

14. Maximum Likelihood Classifier

15. Output Stage In output stage, we define the level of classification.
Create final classes.
Accuracy Assessment
Area estimation.

16. Supervised vs. Unsupervised
Select Training fields
Run clustering algorithm
Edit/evaluate signatures
Identify classes
Classify image
Edit/evaluate signatures
Evaluate classification
Evaluate classification

17. Questions & Discussion