Map of the Great Divide Basin, Wyoming, created using a neural network and used to find likely fossil beds See: http://earthobservatory.nasa.gov/IOTD/view.php?id=79412&src=eoa-iotd

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

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.

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.

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

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

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

Training Data Training Site Selection Resulting Classification

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

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

Parallelpiped “boxes” in 3D feature space

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.

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

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

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

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

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

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

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)

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)

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

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!