accuracy assessment of remote sensing data
DESCRIPTION
Types of accuracy in satellite image classification are discussed and steps to calculate accuracyTRANSCRIPT
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ACCURACY ASSESSMENTACCURACY ASSESSMENT
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ACCURACY ASSESSMENT
– Assess accuracy of a remote sensing output is one of the most important steps in any classification exercise!!
– Without an accuracy assessment the output or
results is of little value
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Goals:
Assess how well a classification worked
Understand how to interpret the usefulness of someone else’s classification
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Nature of Classification
1) Class definition problems
2) Inappropriate class labels
3) Mislabeling of classes
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Accuracy Assessment
Overview
Collect reference data: “ground truth”
Determination of class types at specific locations
Compare reference to classified map
Does class type on classified map = class type determined from reference data?
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Reference Data
Some possible sources
Aerial photo interpretation
Ground truth with GPS
GIS layers
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Reference Data
Issue 1: Choosing reference source
Make sure you can actually extract from the reference source the information that you need for the classification scheme
I.e. Aerial photos may not be good reference data if your classification scheme distinguishes four species of grass. You may need GPS’d ground data.
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Reference data
Issue 2: Determining size of reference plots
Match spatial scale of reference plots and remotely-sensed data
I.e. GPS’d ground plots 5 meters on a side may not be useful if remotely-sensed cells are 1km on a side. You may need aerial photos or even other satellite images.
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Sampling Methods
Simple Random Sampling:observations are randomly placed.
Stratified Random Samplingminimum number of observationsare randomly placed in eachcategory.
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Sampling Methods
Systematic SamplingObservations are placed at equal intervalsaccording to a strategy.
Systematic Non-Aligned Sampling:
a grid provides even distribution ofrandomly placed observations.
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Sampling Methods
Cluster SamplingCluster Sampling
Randomlyplaced “centroids” used as a baseof several nearby observations. The nearby observations can berandomly selected, systematicallyselected, etc...
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Error matrix
Summarize using an error matrix
Class types determined from
reference sourcereference source
Class types determined
from classified
image
# Plots Cotton Sugarcane Fodder Totals
Cotton 50 5 2 57
Sugarcane 14 13 0 27
Fodder 3 5 8 16
Totals 67 23 10 100
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Total Accuracy
Quantifying accuracy– Total Accuracy: Number of correct plots / total number of
plotsClass types determined from
reference sourcereference source
Class types
determined from
classified map
# Plots Cotton Sugarcane Fodder Totals
Cotton 50 5 2 57
Sugarcane 14 13 0 27
Fodder 3 5 8 16
Totals 67 23 10 100
%71100*100
81350 =++=AccuracyTotal
Diagonals represent sites classified correctly according to reference data
Off-diagonals were mis-classified
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Total Accuracy
Problem with Total Accuracy:Summary value is an average
Does not reveal if error was evenly distributed between classes or if some classes were really bad and some really good
Therefore, include other forms:User’s accuracyProducer’s accuracy
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User’s and producer’s accuracy and types of error
User’s accuracy corresponds to error of commission (inclusion):
Producer’s accuracy corresponds to error of omission (exclusion):
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User’s Accuracy
From the perspective of the user of the classified map, how accurate is the map? For a given class, how many of the pixels on the
map are actually what they say they are?– Calculated as:
Number correctly identified in a given map class /
Number claimed to be in that map class
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User’s Accuracy
Class types determined from
reference sourcereference source
Class types
determined from
classified map
# Plots Cotton Sugarcane Fodder Totals
Cotton 50 5 2 57
Sugarcane 14 13 0 27
Fodder 3 5 8 16
Totals 67 23 10 100
%88100*57
50,'
==AccuracyCottonsUser
Example: Cotton
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Producer’s Accuracy
From the perspective of the maker of the classified map, how accurate is the map? For a given class in reference plots, how many of
the pixels on the map are labeled correctly?
– Calculated as:
Number correctly identified in ref. plots of a given class /
Number actually in that reference class
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Producer’s AccuracyClass types determined from
reference sourcereference source
Class types
determined from
classified image
# Plots Conifer sugarcane Fodder Totals
Cotton 50 5 2 57
sugarcane 14 13 0 27
Fodder 3 5 8 16
Totals 67 23 10 100
%75100*67
50'
==Accuracys,Cottonproducer
Example: Cotton
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Summary so far
Class types determined from
reference sourcereference sourceUser’s AccuracyClass types
determined from classified
Image
# Plots Cotton Sugarcane Fodder Totals
Cotton 50 5 2 57 88%Sugarcane 14 13 0 27 48%Fodder 3 5 8 16 50%
Totals 67 23 10 100
Producer’s Accuracy 75% 57% 80% Total: 71%
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Accuracy Assessment in ERDAS Imagine
Make a file in Excel with ONLY the X-Y coordinates for your GPS points collected in field
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Now save this new file as a .txt file (Tab delimited)
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In ERDAS From the Accuracy Assessment window
go to Edit > Import User-defined Points.
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Put GPS co-ordinates file and click ok the window opens as:
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Now you can enter your class values for the reference numbers in the Accuracy Assessment table
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Go to Edit > Show Class Values and the Class column will be filled in with the values that are the actual values of the pixels located at the X-Y coordinates that you indicated
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Finally, you can look at the statistics of your accuracy assessment by first going to Report > Options. Then go to Report > Accuracy Report and the following table will appear
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References
• Lillesand and Kiefer, Chapter 7
• Congalton, R. G. and K. Green. 1999. Assessing the accuracy of remotely sensed data: Principles and practices. Lewis Publishers, Boca Raton.
• Congalton, R.G. 1991. A review of assessing the accuracy of classification of remotely sensed data. Remote Sensing of Environment 37:35-46
• ERDAS, (2005). ERDAS 9.1, Field Guide. Geospatial Imaging, LLC Norcross, Georgia.
• Van Neil, T.G. and McVicar, T.R., (2004) Current and potential uses of optical remote sensing in rice based irrigation systems: A review, Australian Journal of Agricultural Research, 55, 155-85, CSIRO