classification of remotely sensed data general classification concepts unsupervised classifications
TRANSCRIPT
Classification of Remotely Sensed Data
General Classification ConceptsUnsupervised Classifications
Learning Objectives• What is image classification?• What are the three general classification
strategies?• What are the main steps required to classify
images? • 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
U.S. Landcover - MODIS
USGS National Land Cover Database (2006)
• Based on Landsat TM data
• Used CART• Legend is not
aggregated – not as detailed as GAP
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
• Can use computers for on-screen interpretation
Detailed view of Wyoming GAP Land Cover Map
1. Field reconnaissance2. Development of a classification scheme (legend)3. Image enhancement (as needed)4. Classification using manual or digital techniques
Incorporation of ancillary data (as needed)
5. Accuracy assessment6. Iterative refinement
General Classification Steps
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 once in a while
What characteristics of this landscape might be important for making a 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)
1) Must be useful (how will map be used?)2) Must be detectable using the data you have3) Usually hierarchical4) Categories must be mutually exclusive5) Require explicit definitions of each class
Classification Scheme -- Example
I. VegetatedA. Forest
1. Evergreena. Spruce-fir forest
i. Spruce-fir with winterberry understory
b. Lodgepole pine forestc. etc.
2. Deciduous
B. Shrubland
II. Non-Vegetated
Groups
Generate a classification scheme for mapping the main UW campus
using Google Earth imagery
Digital classification
• Creating thematic classes based on groups of similar digital numbers (DNs)– Statistical clustering of the data (lumping
spectrally similar pixels into the same class)– Spectral vs. informational classes– Sometimes combine spectral classes together
to make informational classes
Use many bands at once to create a map of classes
Classification
Classification Strategy• 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
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Band X0 Max
Max
Class
Ban
d Y
Unsupervised Classification 3-bands
1 pixel
1 Class
Unsupervised Classification• Choose bands, indices, enhancements, etc.
that highlight differences in your classes• Decide how many groups (classes) you want• Choose a grouping algorithm
– Simple clustering, K-means, etc.• Classify the image• 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
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 together
• 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.