1. Classification of Remotely Sensed Data
General Classification Concepts
Unsupervised Classifications

2. Learning Objectives What is image classification?
What are the three broad classification strategies?
What are the general steps required to classify images?
What is a classification scheme, and how is a good one constructed?
What is unsupervised classification, and what are its advantages and disadvantages?

3. Wyoming land cover modified from USGS Gap Analysis Program data
Based on Landsat TM data
Used a supervised classification technique called CART analysis
Legend is aggregated from much more detailed legend

4. USGS National Land Cover Database (2006)
Based on Landsat TM data
Used CART
Legend is not aggregated – not as detailed as GAP
U.S. Landcover - MODIS

5. What is Image Classification?
Process of grouping image pixels into classes that represent self-similar features or themes
Analogous to any classification exercise.

6. How? Three general approaches:
Manual interpretation (e.g., photointerpretation, “heads-up digitizing”)
Unsupervised classification of digital data
Supervised classification of digital data
Sequence of methods similar for all three.

7. A word about manual classification
Manual image interpretation was at the core of remote sensing for much of its history
Still perfectly appropriate today in some situations
Usually requires people trained in photointerpretation to make decisions about boundary placement and class labels
Can use computers for on-screen interpretation

9. Detailed view of Wyoming GAP Land Cover Map

10. General Classification Steps
Field reconnaissance
Development of a classification scheme (legend)
Image enhancement/preprocessing (as needed)
Classification using manual or digital techniques
Incorporation of ancillary data (as needed)
Accuracy assessment
Iterative refinement

11. From: http://gisgeography
From: (website no longer active)

12. Field Reconnaissance
Critical for understanding the distribution of your theme in the real world
Understanding of ecology, geology, geomorphology, etc.
Helps you choose useful ancillary data
Useful for interpreting the imagery
Nice excuse to get out of the office once in a while

13. What characteristics of this landscape might be important for making a landcover map using satellite data?

14. Developing Classification Schemes (Legends)
How many types do you want to map?
How should you divide up the feature you are interested in?
Can be very controversial!

15. Classification Schemes (List of types to map)
Must be useful (how will map be used?)
Must be detectable using the data you have
Usually hierarchical
Categories must be mutually exclusive
Require explicit definitions of each class

17. Classification Scheme -- Example
Vegetated
Forest
Evergreen
Spruce-fir forest
i. Spruce-fir with winterberry understory
Lodgepole pine forest
etc.
Deciduous
Shrubland
Non-Vegetated

18. Groups Generate a classification scheme for mapping the main UW campus based on Google Earth imagery

19. Digital classification
Creating thematic classes based on grouping pixels with similar digital numbers (DNs)
Statistical grouping of the data (puts spectrally similar pixels into the same class)
Spectral vs. informational classes
Sometimes combine spectral classes to make informational classes

20. Use many bands at once to create a map of classes
Classification
Use many bands at once to create a map of classes

21. Classification Strategies
Unsupervised – computer clusters pixels based only on the similarity of their DNs.
Supervised – computer uses training data—examples of target classes—and assigns pixels to the closest training class using similarity
Others (neural networks, fuzzy logic etc.)

22. Unsupervised Classification 2-bands
Max . . . . . . . . . .
Class . . . . . . . . . . . . . . . . . . . . . . . . . .
Band Y . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Band X
Max

23. Unsupervised Classification 3-bands
1 pixel
1 Class

24. Unsupervised Classification
Decide how many groups (classes) you want
Choose bands, indices, enhancements, etc. that highlight differences in your classes
Choose a grouping algorithm
Simple clustering, K-means, etc.
Classify the image (run the algorithm)
Label (and aggregate) classes and evaluate the results

25. Advantages of Unsupervised Classifications
No extensive prior knowledge of map area required (but you have to label the classes!)
Classes are based only on spectral information (spectral classes), so not as subjective as when humans make decisions
Can use results of unsupervised classification to help guide you in the field, even if ultimately you use supervised classification.

26. Disadvantages of Unsupervised Classifications
Spectral classes do not always correspond to informational classes
Spectral properties of informational classes change over time so you can’t always use same class statistics when moving from one image to another

27. Grouping Algorithms Statistical routines for grouping similar pixels
Operate in feature space
Differ in how they:
Determine what is similar (distance measures)
Determine the statistical center (centroid) of a class

28. Unsupervised classification of Martian terrain.
From Stepinski and Bagaria IEEE Geoscience and Remote Sensing Letters.