Supervised Classification

1. Supervised Classification Mirza Muhammad Waqar Contact: mirza.waqar@ist.edu.pk +92-21-34650765-79 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

3. Hardvs 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 • Inter-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