Classification of Remotely Sensed Data General Classification Concepts Unsupervised Classifications

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?

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

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

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

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.

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

Detailed view of Wyoming GAP Land Cover Map

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

From: http://gisgeography From: http://gisgeography.com/image-classification-techniques-remote-sensing/ (website no longer active)

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

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

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!

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

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

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

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

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

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.)

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

Unsupervised Classification 3-bands 1 pixel 1 Class

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

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.

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

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

Unsupervised classification of Martian terrain. From Stepinski and Bagaria. 2009. IEEE Geoscience and Remote Sensing Letters.