This document summarizes a survey of accuracy assessment methods used in object-based image analysis (OBIA). The survey examined 100 papers that used OBIA for land cover classification. It found that 88% of the papers performed some type of accuracy assessment, most commonly comparing the OBIA outputs visually to the original imagery. For sampling design, most papers used points or polygons as the sampling unit, with around 50 total samples on average. Reference data was typically from the same year as the imagery and independently validated in around a third of studies.
1. A survey of
Accuracy assessment measures
in OBIA
Rahul Rakshit
rrakshit@clarku.edu
Graduate School of Geography
Clark University
hero.clarku.edu/holmes
Graduate School of Geography, Clark University 1
2. Background: Pixel Vs Object based accuracy estimates
Non
Legend Forest
Forest
Classification Reference Accuracy
Assessment
Pixel 1
Pixel 1 Pixel 1 Inaccurate
Classification
Object 1
Object 1 Object 1 Inaccurate
Geometry
Object 1
Object 1 Object 1 Inaccurate
Classification
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3. Background: Selecting the sampling unit - Point
Land-cover
1. Impervious
2. Grass
3. Bare Soil
4. Coniferous
5. Deciduous
Scale 1:600
1:150
Graduate School of Geography, Clark University 3
4. Background: Selecting the sampling unit - Polygon
Land-cover
1. Impervious
2. Grass
3. Bare Soil
4. Coniferous
5. Deciduous
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5. Background: Positional Accuracy of a GPS
Land-cover
1. Impervious
2. Grass
3. Bare Soil
4. Coniferous
5. Deciduous
WAAS
Wide Area Augmentation System 5 m accuracy
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6. Selection Criteria: 100 papers
Studies that have used OBIA as a classification tool to create thematic maps
70 69
60
•IJRS
No. of Papers
50
•PE&RS
40
•RS of Env
30 24 •Sensors
20 •CJRS
10 7 •Landscape & Planning
0
Conference Proceedings Book Chapters Peer Reviewed Articles
Source
30
30
No. of Papers
20 15 14
10
10 7 8 8
4 3
1
0
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Year
Graduate School of Geography, Clark University 6
7. Remote sensing accuracy assessment
Response Design
Method for determining the reference class
Sampling Design
Method for choosing locations at which reference class will be determined
Analysis
Agreement between classified Vs reference datasets, results
Stehman (1998), Congalton and Green (2008)
Graduate School of Geography, Clark University 7
8. Survey Attributes
Data Description
●Paper Authors Year of Publication ●Accuracy Assessment Performed
Classification Design
●Theme Year of Data Sensor Resolution No. of Classes ●Software
Segmentation Properties
●Segmentation Algorithm Segmentation Scale
Sampling Design
●Sampling Method Total samples Sampling Units No. of samples per class
Response Design
●Reference Data Source Independent validation source
●Difference in years between reference and training data
Analysis
●Error Matrix Alternate error estimation Overall Accuracy
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9. Sample Data
AA = accuracy assessment Blank Space = no data available
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10. Results:
12%
88%
Accuracy assessment permformed Accuracy assessment not permformed
• Accuracy estimated as sufficient/reasonable.
• Accuracy measured by visually comparing the OBIA outputs with the imagery.
• Accuracy assessment to be conducted in the future.
Graduate School of Geography, Clark University 10
11. Sampling Design
Sampling Unit
Accessible
1%
37 39
24
Points Polygons No Information
No. of Samples
13
50 50
Total no. of samples No Information
Sampling Method No. of samples per class
Graduate School of Geography, Clark University 11
12. Response Design
Independent source of validation
34
56
10
Yes No No Information
28
72
Source of reference data Year of Reference Data No Information
Graduate School of Geography, Clark University 12
13. Analysis
Graduate School of Geography, Clark University 13
14. Conclusions
1. Most of the studies do not mention the sampling method
2. Both points and polygons are used as sampling units
3. Almost half of the studies do not provide information on total
number of samples
4. Most of the studies use field data (GPS) as reference datasets
5. Majority of the studies separate validation data from
classification data
6. Majority of the studies do not provide information on year of
reference datasets
7. Majority of the studies use the error matrix to show accuracy
assessment results
8. Some studies have visually estimated the agreement between
classified and reference data
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15. Recommendations:
• Object geometry accuracy can be quantified by tools such as LIST (Landscape
Interpretation Support Tool) and CI (Comparison Index) that use polygon overlay
• Data quality: Jarlath O’Neil Dunne http://letters-sal.blogspot.com/2010/08/is-peer-reviewed-literaturethe-best.html
• Temporal difference between classification and reference datasets should be kept
to minimum
• Avoid misregistration (low positional accuracy)
• Take spatially well distributed samples (spatial autocorrelation)
• Use stratified random sampling
• Specify sample size for each stratum
• Use thematic maps as reference datasets with caution
• Mask out training data from sampling design
• Hybrid point and polygon sampling approach (Albert Decatur: Upcoming)
Graduate School of Geography, Clark University 15
16. Acknowledgements
• Prof. Robert Gilmore Pontius, Jr.
• Prof. Colin Polsky
• Prof. John Rogan
• Albert Decatur
• Shitij Mehta
• Jarlath O’ Neil Dunne
More Information: rrakshit@clarku.edu
http://hero.clarku.edu/holmes
This material is based upon work supported by the National Science Foundation (NSF) under grant Nos. BCS-0709685 (Coupled Natural-
Human Systems), OCE-0423565 (Long-Term Ecological Research), SES-0849985 (REU Site), and BCS-0948984 (ULTRA-ex), and by the
Clark University O'Connor '78 Endowment. Any opinions, findings and conclusions or recommendations expressed in this material are
those of the author(s) and do not necessarily reflect the views of the funders.
Graduate School of Geography, Clark University 16
Editor's Notes
I work in the holmes project in Clark university and we produce land-cover maps. We use object based image analysis to create these maps from images that are 45 cms in resolution. We have mapped more than 4 billion pixels in our study area.We selected OBIA method to produce land-cover maps with considerable accuracy when compared to the pixel based methods.While working on this project we realized that conducting an accuracy assessment of maps produced by OBIA techniques is a little harder and different form the traditional pixel based validation techniques. Therefore a survey was done to find out how different studies in the last 10 years have conducted their validation exercise when they have used OBIAas a classification tool.
Now let us see how the OBIA method is different from that of thePixel based method.
The other factor in OBIA accuracy assessment is the choice of the sampling units. The sampling units can be either points or polygons.Traditionally random or stratified random points are generated on the landscape and then the points are visited either on the field or on a high resolution image and then the category of the point is noted at a particular scale as seen in this image. The scale is 1:600.It is a difficult task to assign one of the 5 land-cover categories to the point. Even after zooming in at 4 times the previous scale it is still not clear.
Now let us take the example of a polygon a the sampling units. Polygons are selected randomly or stratified randomly. These polygons or segments are created using a particular scale. That scale may produce pure of mixed segments even for the same land-cover category. As seen in this example the segment in red is selected randomly but it can consist of more than one category because of the scale factor. Therefore, it is difficult to assign one single category to this polygon.
One more thing to consider during accuracy assessment is the use of GPS to collect field data. Even with a WAAS enabled GPS that has an enhanced accuracy: the accuracy is till 5 m. In a heterogeneous urban landscape, the accuracy of 5 m can result confusion on which LC category to be assigned to the point when field data is collected.From our personal experience when we went to do the fieldwork, three persons with GPS units stood in the edges of this circle with the same co-ordinates (upto 2 decimals places). Each of them was standing on a different land-cover category.
On the basis of these points we decided to do a survey of existing literature that have used OBIA as a classification tool to create thematic maps.100 papers are used in the survey.The majority of the studies mapped land-cover, followed by vegetation mapping. Mapping impervious surfaces was also one the major themes in these papers. The papers are seleted from the following sources and their yearwise distribution is as follows.
Positional accuracy for field data.Only one out of the 100 papers stated that some of the differences in agreement between the classification and the reference data can be attributed to the difference in time(4 years in their case) between the images.