1. Map of the Great Divide Basin, Wyoming, created using a neural network and used to find likely fossil beds
See:

2. Supervised Classifications & Miscellaneous Classification Techniques
Using training data to classify digital imagery

3. Learning Objectives
What is supervised classification and how is it different than unsupervised?
What are training data, and how are they developed and used?
Understand several supervised classification algorithms and how they work.
Understand that there are many unsupervised algorithms besides the ones discussed in this class.

4. How does supervised classification work?
Associate areas on the image with informational classes from the field (training data!)
Generate statistics to describe the spectral characteristics of each informational class in terms of the satellite bands and/or enhancements
Assign unknown pixels to classes based on similarity to the statistical description of the training class.

5. Supervised vs. Unsupervised Classifications
Choose Bands, enhancements, etc.
Collect training data for your map area
Cluster pixels into spectral classes
Choose bands, enhancements, etc.
Label clusters corresponding to informational classes
Assign pixels to most similar informational class
Evaluate result
Evaluate result

6. Supervised Classification Process
Landsat image near Riverton, WY A
A = sagebrush B
B = water
C = agriculture C
D = riparian vegetation D

7. Selecting Training Data
Selection of training data is the most important part of a supervised classification!
Training data must:
Represent all of the classes that you want to map
Represent the variability within classes (spectral and environmental)
(Can split informational classes for classification purposes)
Be carefully selected based on field work and examination of the image
Be modified iteratively if necessary to improve your classification

8. Training Data
Training Site Selection
Resulting Classification

9. Supervised Classification Algorithms
There are many techniques for assigning pixels to training (informational) classes. Common methods include:
Parallelpiped
Minimum Distance
Maximum Likelihood

10. Parallelpiped Classifier
Determine the range of DNs for each class in each band
Use these DN ranges to define multi-dimensional “boxes” (parallelpipeds) in feature space
If a pixel falls within a box, it is assumed to belong to that class
If a pixel falls outside of all boxes, it is not classified

11. Parallelpiped “boxes” in 3D feature space

12. Pros/Cons of Parallelpiped Classifier
Does NOT assign every pixel to a class. Only the pixels that fall within ranges.
Good for helping decide if you need additional classes (if there are unclassified pixels)
Good for helping decide if you need additional predictor variables (spectral or ancillary)
Problems when class ranges overlap—must develop rules to deal with overlap areas, or refine the training data.

13. Minimum Distance Classifier
Assigns pixels to the class they are closest to in feature space in terms of Euclidean distance.
Calculate the average DN for each class across all bands (= the class centroid)
Calculate the Euclidean distance from each pixel to each centroid in feature space
Assign each pixel to the class with the closest centroid

14. Use Pythagorean theorem to calculate distance (in terms of DNs) to each centroid. Assign unknown pixel to closest centroid.
Class 1 centroid
Unknown pixel
Class 2 centroid
Band Y
Class 3 centroid
Band X

15. Pros/Cons of Minimum Distance Classifier
Classifies every pixel in the image (regardless of probability that it is really in a class)
Does not explicitly consider the variability (variance) within classes (acts as if all classes have same variance).
Works well for some images and not as well for others

16. Maximum Likelihood Classifier
Assigns unknown pixels to the class that it has the highest probability of belonging to
Based on how many standard deviations the pixel is from the class centroid (variance of the class)
Should use with normally distributed data (bell-shaped histograms) but we are often permissive about deviation from this

18. Pros/Cons of Maximum Likelihood Classifier
Classifies every pixel in the image
Recognizes that some classes have lots of spectral variability and are more likely to include pixels that are “far” from the class centroid
Image data are not always normally distributed
Often, but not always, a better choice than minimum distance classifiers

19. Supervised Classification--Summary
Supervised classification uses knowledge of the locations of informational classes to group pixels
Requires close attention to development of training data
Typically results in better maps than unsupervised classifications IF you have good training data.
Requires more work (time/money) than unsupervised classifications

20. Other Classification Algorithms
Fuzzy classifiers
Decision trees
Classification and Regression Trees (CART)
Object-based image classification
Many others – neural networks, expert systems, etc.
Some of these are covered in Advanced RS (BOT/GEOG 4211/5211)

21. Fuzzy Classifications
Recognize that in the real world, distinctions between classes are often not distinct
Assigns the same place on the ground to all classes (with a membership probability)

23. Decision Trees
Similar to a dichotomous key that you might use to key out plants or animals
Can combine many types of data to create classes (e.g., spectral data, elevation data, soil maps, etc.)
Can be built “by hand” or using statistical techniques
Includes CARTs that build trees based on statistics

25. Image Classification Philosophy
What is it that makes one feature of interest different from another?
Can you capture that difference accurately with spatially distributed data?
How can you best exploit the differences between features statistically or otherwise?
Remember that we must often go beyond just using spectral data!