1. Expert/rule based classification
The topic of my research is Georferencing of images by exploiting geometric distortions in stereo images of UK DMC. And this is my preliminary defense presentation.
Contact:
Mirza Muhammad Waqar

2. Lecture Overview Image classification basics Image and feature spaces
Supervised vs Unsupervised classification
Basic concepts of Expert classification
Imagine’ Expert Classifier
Knowledge Engineer
Setting up of rules
Output evaluation
Final notes

3. Remote Sensing Process

4. Snow Vs. Clouds

5. Snow Vs. Clouds Clouds scatter at all wavelengths
Snow absorbs at >1.4 mm c

6. Spatial Resolution Spatial Resolution / Pixel size
Spectral Resolution /
Number of bands
Multispectral
< 10 bands
AVHRR: 1km
Hyperspectral
10’s to 100’s of bands
Landsat: 30m
SPOT: 10m
Quickbird: 2m
Sub-meter
Time

9. Generalized Workflow

10. Image Classification What is it ? Grouping of similar features
Separation of dissimilar ones
Assigning class label to pixels
Resulting in manageable size of classes

11. Image Classification (2)

12. Image Classification (3)

13. Image Space

14. Feature Space

15. Feature Space (2) Two dimensional graph of scatter plot
Formation of cluster of points representing DN values of two spectral bands
Each cluster of points corresponds to a certain cover type on ground
Scatter plot of two bands

16. Supervised Classification
Satellite Image (Large Area)
Ground Truth (Small Area)
Thematic Map (Large Area)

17. Supervised vs Unsupervised

18. Limitations of Fundamental Methods
One of the drawbacks of the fundamental methods is that the only information utilized is that contained in the image itself, in the form of one or more channels. Nothing else is considered.
On the contrary, human interpreters of hard copy images can integrate many other types of information.
While working on a image, they may also take into consideration such information as that from available thematic maps, personal field experience, common sense, etc.

19. Limitations of Fundamental Methods (2)
When natural objectives, such as landscape and forest, are concerned, relations among the objects and their surroundings can be used by specialists with corresponding knowledge.
As known from forestry and ecology, there exist certain relations between plants and their environmental conditions.
Plants grow best where the environmental conditions are most favorable for their specific adaptabilities. Information about the distribution of the plant objects can be exploited from their environmental conditions.

20. Overview of Classification Methods

21. What is Expert, or Rule-based Classification?
Expert knowledge can be represented in form of rules:
if condition then inference
Complex combinations of rules can be built (knowledge base)
Can be applied on both pixel and object (region) base
Although we use Erdas Imagine Expert Classifier as an example, the principles are generic

22. Rules Definition
The base contains various "hard rules", i.e., the rules with no probability or belief weight. They are usually of the form of “IF···, THEN··· “
Another kind of rules are "fuzzy rules“, which go with a value of probability or belief weight.
The hard rules are usually based on common knowledge or common sense;
While the fuzzy rules are obtained through interviewing and questionnaires from experts

23. Expert Classification
We can employ a similar approach to eCognition at the pixel level, using Imagine’s Expert Classifier
It is argued that the quality of an image classification increases with the amount of information we have available
Landcover classification

24. Expert Classification (2)
We may further want to perform an analysis that goes beyond a mere identification of a landcover
Example: analyze a terrain in terms of the mobility it allows. Identify different more or less easily traversable pixels, ranging from wide roads (easy) to forest (slow) to water or buildings (no go).

25. Expert Classification (3)
This is what the Expert Classifier does, in a way that is more akin to GIS analysis than traditional classification
The software uses a rule-based approach with a hierarchy of rules, user-defined variables, and information sources as varied as raster imagery, vector files, graphic models or external programs
It is similar to the logic and structure of the graphic models that can be constructed in Imagine, but provides a better frame to follow complex rules and decisions

26. Expert Classifier
The expert classifier is integrated into the Classifier module, and consists of 2 parts, the Knowledge Engineer and the Knowledge Classifier
The Knowledge Engineer provides an interface for an expert, while the Knowledge Classifier can be used by an non-expert to execute an existing Classification Knowledge Base
There is virtually no limit to complexity, although understanding the logic of a multitude of rules is not always easy, and neither is translating a research question into the appropriate hypotheses and rules

27. Expert Classifier - Layout
Decision Tree
Overview
The main work (and close-up) window
The knowledge base component

28. Decision Trees
The classifier is composed of decision tree branches, i.e. a hypothesis, a rule (or more), and one or more conditions
Hypothesis
Rule
Condition
And Or

29. Decision Trees (2)
Translating rules and hypotheses into algebra can be difficult

30. Example of Decision Tree
Overview
The main work (and close-up) window
The knowledge base component

31. Example of Decision Tree (2)
Note that the hypotheses don’t lead to a single answer, but rather to pixels of particular value in a new, resulting thematic image file.
This means that the result of the analysis is a classified raster image
The objective in the following example was to identify terrain suitable for easy traversing
Input data consist of (i) the result of a supervised ML classification, (ii) a DEM, (iii) a map of major and minor roads, and (iv) a georeferenced aerial photograph. In addition, several existing graphic models are used

32. Decision Trees – A Small Exercise:
Exercise 1: We want to find areas to grow grapes. Those need a southern exposure, preferably a hill side, and good rainfall. What data do we need and how do we construct the knowledge base?
We need:
Elevation data
Rainfall information

33. Expert Classification Output
So what do we get from Expert Classifier? A simple thematic map, where each pixel is assigned a class
Satisfaction of one of several rules leads to acceptance of hypothesis – so how do we determine which data source to chose?
The hypothesis with the highest confidence is chosen
Sometimes we have data sources of different quality/reliability

34. We can selectively turn off individual classes
Output Evaluation
Expert Classifier provides a powerful way to evaluating the results
Analysis can be run in ‘Test-mode’
We can selectively turn off individual classes
We can also produce a confidence image to assess the quality of our classification

35. Confidence Image We can also create the confidence image
Good to evaluate which rules may have to be revised

36. Output Evaluation (2)
For testing we can also turn off individual hypotheses within the workspace

37. Output Evaluation (3)
We can also evaluate the classified image, and “back-track” how a pixel came to be classified

38. Knowledge Classifier
The knowledge base created can then also be used by non-experts in the Knowledge Classifier

39. Final Notes A rule-based system has obvious strengths and limitations.
If the entire knowledge in a particular domain can be encoded in a finite set of rules, then a rule-based system is effective.
On the other hand, if there are too many rules, it becomes difficult to maintain the system.

40. Final Notes (2)
A well-constructed and useful knowledge base is a challenging and involved task
Several refinements and iterations are likely required
But these knowledge bases also serve as basic of expert systems where non-experts can do the analysis – getting it right is vital!
The key is to understand the pros & cons of different classification methods, and then chose accordingly

41. Questions & Discussion