1. Remote Sensing Unsupervised Image Classification

2. 1. Unsupervised Image Classification ► The process requires a minimal amount of initial input from the analyst ► A numeric operation searches for natural grouping of the spectral properties of pixels ► The analyst determines the information class for each spectral class after the spectral classes are formed

3. 1. Unsupervised Classification ► Chain method ► ISODATA ► Spectral mixture analysis ► Object-based image analysis

4. 2. Chain Method ► Pass 1 builds clusters and calculates their mean vectors ► Pass 2 assigns pixels to clusters based on the minimum-distance rule

6. Pass 1. Cluster Building ► R, a spectral radius used to determine whether a new cluster should be formed (e.g., 15 brightness) ► N, the number of pixels to be evaluated between each major merging of the clusters (e.g., 2000)

7. Pass 1. Cluster Building ► C, a spectral distance used to determine merging clusters when N is reached (e.g., 30 brightness) ► Cmax, the maximum number of spectral clusters (categories) (e.g., 20) to be identified

8. Pass 1 ► The operation evaluates pixels sequentially, combining successive pixels into a cluster if their spectral distance < R ► A cluster is complete when N is reached ► If the spectral distance between two clusters is < C, the two clusters are merged, until no clusters with distance < C ► The new mean is the weighted average of the two original clusters

9. Pass 2. Assigning Pixels ► Assigns pixels based on the minimum distance classifier ► Manual modification based on knowledge of the area, co- spectral plots, and interactive display

12. 3. ISODATA Method ► Iterative Self Organizing Data Analysis Technique

13. ISODATA Parameters required: ► Cmax, the maximum number of spectral clusters ► T, maximum % of pixels whose classes are allowed to be unchanged between iterations ► M, the max number of times of classifying pixels and calculating cluster mean vectors

14. ISODATA ► Minimum members in a cluster (%). For example, if the % <0.01, the cluster is deleted ► Maximum Std Dev, when a std dev > specified Max-std-dev and the number of members > 2*Min members, the cluster is split

15. ISODATA ► Split separation: when the value is changed from 0.0, it replaces Std Dev to determine the locations of the new mean vectors plus and minus this split separation value ► Minimum distance between cluster means. Clusters with a weighted distance < this value (e.g., 3.0) are merged

16. ISODATA ► It uses a large number of passes ► The initial means are determined based on the mean and std dev of each band

18. ► http://www.youtube.com/watch?v=ikArEGp- dv0 http://www.youtube.com/watch?v=ikArEGp- dv0 http://www.youtube.com/watch?v=ikArEGp- dv0

19. Iterations ► Assigns each pixel using the minimum distance classifier ► The second to M th iteration re-calculate the mean vectors examine Min members(%) Max std dev Max std dev split separation split separation Min distance between clusters Min distance between clusters ► The iteration stops when T or M is reached

20. Readings ► Jensen 1996. 2 nd Edition or 2005 3 rd Edition, Introductory Digital Image Processing. Prentice Hall.

21. 4. Classification of Mixed Pixels ► Mixed pixels - when a sensor’s IFOV covers more than one land cover feature e.g. tree leaves, grass, and bare soil ► Depends on the spatial resolution of sensors and the scale of features ► Sub-pixel classification - spectral mixture analysis - spectral mixture analysis

22. Spectral Mixture Analysis ► Mixed spectral signatures are compared to pure reference spectra ► The pure signature is measured in the lab, field, or from images ► Assuming that the variation in an image is a mixture of a limited number of features ► Estimates approx proportion of each pure feature in a pixel

23. Spectral Mixture Analysis.. ► Linear mixture models - assuming a linear mixture of pure features ► Endmembers - the pure reference signatures ► Weight - the proportion of the area occupied by an endmember ► Output - fraction image for each endmember showing the fraction occupied by an endmember in a pixel

24. Spectral Mixture Analysis.. Gap, water, Mangrove, forest Kemal G o kkaya 2008

25. Tole L., 2008. Changes in the built vs. non-built environment in a rapidly urbanizing region: A case study of the Greater Toronto Area, Computers, Environment and Urban Systems, 32(5): 355-364.

26. Spectral Mixture Analysis.. ► Two basic conditions ► I. The sum of fractions of all endmembers in a pixel must equal 1  F i = F 1 + F 2 + … + F n = 1 ► II. The DN of a pixel is the sum of the DNs of endmembers weighted by their area fractions D  = F 1 D  1 + F 2 D  2 + … + F n D  n +E D  = F 1 D  1 + F 2 D  2 + … + F n D  n +E

27. Spectral Mixture Analysis.. ► One D   equation for each band, plus one  F i equation for all bands ► Number of endmembers = number of bands + 1 One exact solution without the E term One exact solution without the E term ► Number of endmembers < number of bands +1 Fs and E can be estimated statistically Fs and E can be estimated statistically ► Number of endmembers > number of bands +1 No unique solution No unique solution

28. Spectral Mixture Analysis.. ► Advantages/characteristics - a realistic representation of features - a deterministic, not a statistic, method - fuzzy set theory vs. fuzzy classification ► Disadvantages - does not account for multiple reflections - does not account for multiple reflections

29. 5. Object-based Classification ► Also called object-based image analysis (OBIA) ► vs. per pixel classification All classifiers so far consider the spectral info of a single pixel regardless of its neighbors All classifiers so far consider the spectral info of a single pixel regardless of its neighbors

31. Kutztown GEOEYE-1 Sean Ahearn, Hunter college

32. 5. Object-based Classification.. ► A two-step process I. segmentation of the image into objects I. segmentation of the image into objects II. Classiication of the objects II. Classiication of the objects Works at multiple scales and uses color, shape, size, texture, pattern, and context information to group pixels into objects

33. 5. Object-based Classification.. ► Two sets of characteristics can be used to classify the objects The characteristics of the object itself (spectral, texture, shape, etc.) The relationship between objects (connectivity, proximity, etc.)

34. 5. Object-based Classification.. ► Advantages ► Disadvantages ► E-cognition, Definiens and Trimble

35. Readings ► Chapter 7