1. Image Classification

2. Assignment
تطبيقات الاستشعار عن بعد في الجيولوجيا

3. Land classification
Aims at label each pixel in a scene to a specific land cover types
Pixels can then be either correctly classified, incorrectly classified or unclassified
Two main type of classification
Unsupervised
Supervised

4. Unsupervised classification
No previous knowledge assumed about the data
Tries to spectrally separate the pixels
User has controls over
No of classes
No of iterations

5. Supervised Image Classification
An image classification procedure that requires interaction with the analyst

6. 1. General Procedures
Training stage  - The analyst identifies the representative training areas (training set) and develops summary statistics for each category
Classification stage  - Each pixel is categorized into a land cover class
 Output stage  - The classified image is presented in GIS or other forms

7. Supervised classification

8. Supervised classification

10. Training

11. Parallelepiped classifier
Classifiers
Minimum distance classifier
Parallelepiped classifier

12. 1. Minimum Distance Classifier
Calculates mean of the spectral values for the training set in each band and for each category
 Measures the distance from a pixel of unknown identify to the mean of each category
 Assigns the pixel to the category with the shortest distance
 Assigns a pixel as "unknown" if the pixel is beyond the distances defined by the analyst

13. 2. Minimum Distance Classifier
(40,60)
0,0

14. 1. Minimum Distance Classifier

15. Minimum Distance Classifier
Advantage  computationally simple and fast
 Disadvantage  insensitive to differences in variance among categories

16. 2. Parallelepiped Classifier
Forms a decision region by the maximum and minimum values of the training set in each band and for each category (class)
 Assigns a pixel to the category where the pixel falls in
 Assigns a pixel as "unknown" if it falls outside of all regions  

17. 2. Parallelepiped Classifier

18. Parallelepiped Classifier
Advantage  computationally simple and fast
  takes differences in variance into account
Disadvantage  performs poorly when the regions overlap because of high correlation between categories (high covariance)  

19. Parallelepiped Classifier?

20. Parallelepiped Classifier