Accuracy assessment is an essential step in any remote sensing classification. It involves collecting reference data through methods like GPS ground sampling and comparing the classifications to determine accuracy. Key aspects of accuracy assessment include error matrices to calculate overall, user's, and producer's accuracies. User's accuracy indicates errors of commission while producer's accuracy reflects errors of omission. ERDAS Imagine software can be used to import reference data and generate accuracy reports to evaluate classification performance.

1. ACCURACY ASSESSMENTACCURACY ASSESSMENT

2. 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

3. Goals:
Assess how well a classification worked
Understand how to interpret the usefulness of
someone else’s classification

4. Nature of Classification
1) Class definition problems
2) Inappropriate class labels
3) Mislabeling of classes

5. 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?

6. Reference Data
Some possible sources
Aerial photo interpretation
Ground truth with GPS
GIS layers

7. 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.

8. 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.

9. Sampling Methods
Simple Random Sampling:
observations are randomly placed.
Stratified Random Sampling
minimum number of observations
are randomly placed in each
category.

10. Sampling Methods
Systematic Sampling
Observations are placed at equal intervals
according to a strategy.
Systematic Non-Aligned Sampling:
a grid provides even distribution of
randomly placed observations.

11. Sampling Methods
Cluster SamplingCluster Sampling
Randomly
placed “centroids” used as a base
of several nearby observations.
The nearby observations can be
randomly selected, systematically
selected, etc...

12. 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

13. Total Accuracy
 Quantifying accuracy
– Total Accuracy: Number of correct plots / total number of
plots
Class types determined from
reference sourcereference source
Class
types
determin
ed from
classifie
d 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

14. 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 accuracy
Producer’s accuracy

15. 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):

16. 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

17. User’s Accuracy
Class types determined from
reference sourcereference source
Class
types
determin
ed from
classifie
d 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
,'
==Accuracy CottonsUser
Example: Cotton

18. 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

19. Producer’s Accuracy
Class types determined from
reference sourcereference source
Class
types
determine
d 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
'
==Accuracy s,Cottonproducer
Example: Cotton

20. Summary so far
Class types determined from
reference sourcereference source
User’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%

21. Accuracy Assessment in ERDAS
Imagine
 Make a file in Excel with ONLY the X-Y
coordinates for your GPS points collected in field

22.  Now save this new file as a .txt file (Tab delimited)

23.  In ERDAS From the Accuracy Assessment window
go to Edit > Import User-defined Points.

24.  Put GPS co-ordinates file and click ok the window
opens as:

25.  Now you can enter your class values for the
reference numbers in the Accuracy Assessment table

26.  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

27.  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

28. 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