Unsupervised Classification

1. Unsupervised Classification Mirza Muhammad Waqar Contact: mirza.waqar@ist.edu.pk +92-21-34650765-79 EXT:2257 RG610 Course: Introduction to RS & DIP

2. Contents • Satellite Image Classification • Spectral vs Spatial Pattern Recognition • Feature Space • Image Spectral Signature • Limitations in Classification • Unsupervised Classification • Clustering Algorithms • K-means Approach • ISODATA Model • Texture or Roughness Model • Post Classification

3. Satellite Image Classification • Automatic categorization of all pixel into land cover classes. • To explore different land covers in a satellite image. • To assign unique no or symbol to particular land cover. • Qualitative and Quantitative analysis of satellite image.

4. Spectral vs Spatial • Spectral pattern recognition Family of classification procedures that utilize pixel by pixel spectral information as the basis for automated land cover classification. • Spatial pattern recognition Categorization of image pixels on the basis of their spatial relationship with pixels surrounding them.

5. Feature Space

6. Feature Space

7. Image Spectral Signatures

8. Satellite Image Classification • Three types of classifications • Un-Supervised Classification • Supervised Classification • Hybrid Classification

9. Limitations in Classification • Limitations face in satellite image classification • Pixel Size • All Statistical parameters are developed for normalized distribution.

11. Un-Supervised Classification • Use to cluster pixels based on statistics only. • No user defined training classes required. • Machine based classification. • Post classification is of more importance to make results meaningful. • Incorporate all the natural groups in satellite image (spectral classes). • Un-supervised Classification have two phases. • Clustering • Post Classification

12. Clustering Algorithms • Numerous clustering algorithms • K-means Approach • ISODATA Model • Texture or Roughness Model

13. K-means Approach • Accept number of clusters to be located in the data. • Arbitrarily locate that number of cluster centers in multi-dimensional measurement space. • Each pixel is assigned to the cluster whose mean vector is closest.

14. Band B Band A

15. Band B New computed Means Previous Means Band A

16. Band B New computed Means Previous Means Band A

17. K-Mean Approach • New means are computed. • Revised clustering on the base of new computed means. • This process continue until there is no significant change in clusters mean.

18. ISODATA Model • Iterative Self-Organizing Data Analysis. • Follow K-mean principle for clustering. • Accept number of clutters, number of iterations & convergence tolerance from the user and form clusters. • Permits number of clusters to change from one iteration to the next, by merging, splitting and deleting clusters based on spatial statistics and user defined conditions.

19. Band B New computed Means Previous Means Band A

21. Texture or Roughness Model • It incorporate a sensitivity to image “texture” or “roughness” as a basis for establishing clusters centers. • Texture is computed through multi-dimensional variance observed in moving window (e.g. a 3x3 window). • Analyst sets a variance threshold below which window is consider “smooth” and above which it is considered “rough”.

22. Texture or Roughness Model • The mean of the first smooth window encountered in the image becomes the first cluster center. • The mean of the second smooth window encountered in the image becomes the second cluster center and so forth. • This process continue until the user defined no of clusters reached.

23. Post Classification • In post classification phase, analyst compare spectral classes with some reference data to determine the identify of the spectral classes. • Spectral reflectance curves can be used to identify the spectral classes. • Defining the level of classification • Merging different classes to reach final outcome. • Accuracy assessment through field truthing.

25. Questions & Discussion