obia accuracy survey
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Graduate School of Geography, Clark University
Rahul Rakshitrrakshit@clarku.eduGraduate School of GeographyClark University
hero.clarku.edu/holmes
1
Accuracy assessment measures in OBIA
A survey of
Background: Pixel Vs Object based accuracy estimates
Object 1 Object 1Object 1
InaccurateGeometry
Object 1 Object 1Object 1
InaccurateClassification
Forest NonForestLegend
Pixel 1 Pixel 1Pixel 1 Inaccurate
Classification
Classification Reference Accuracy Assessment
Graduate School of Geography, Clark University 2
1. Impervious2. Grass3. Bare Soil4. Coniferous5. Deciduous
Land-cover
Scale 1:600Scale 1:150
Background: Selecting the sampling unit - Point
Graduate School of Geography, Clark University 3
1. Impervious2. Grass3. Bare Soil4. Coniferous5. Deciduous
Land-cover
Background: Selecting the sampling unit - Polygon
Graduate School of Geography, Clark University 4
1. Impervious2. Grass3. Bare Soil4. Coniferous5. Deciduous
Land-cover
WAASWide Area Augmentation System 5 m accuracy
Background: Positional Accuracy of a GPS
Graduate School of Geography, Clark University 5
Selection Criteria: 100 papers
2002 2003 2004 2005 2006 2007 2008 2009 2010 20110
10
20
30
1
7 8 8
15
4
30
14
10
3
Year
No.
of P
aper
s
Conference Proceedings Book Chapters Peer Reviewed Articles0
10
20
30
40
50
60
70
24
7
69
Source
No.
of P
aper
s •IJRS•PE&RS•RS of Env•Sensors•CJRS•Landscape & Planning
Studies that have used OBIA as a classification tool to create thematic maps
Graduate School of Geography, Clark University 6
Remote sensing accuracy assessment
Sampling DesignMethod for choosing locations at which reference class will be determined
Response Design
Analysis
Method for determining the reference class
Agreement between classified Vs reference datasets, results
Stehman (1998), Congalton and Green (2008)
Graduate School of Geography, Clark University 7
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
Survey Attributes
Graduate School of Geography, Clark University 8
AA = accuracy assessment Blank Space = no data available
Sample Data
Graduate School of Geography, Clark University 9
88%
12%
Accuracy assessment permformed Accuracy assessment not permformed
Results:
• 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
5050
Total no. of samples No Information
No. of SamplesNo
sam
plin
g met
hod
men
tione
d
60%
Random17%
StratifiedRandom
19%
Stratified 3%Accessible
1%
13
No. of samples per class
Sampling Design
Sampling Method
39
24
37
Points Polygons No Information
Sampling Unit
Graduate School of Geography, Clark University 11
Response Design
No Information
Field Data (GPS)
35%
Image Interpretation
28%
Other ThematicMaps 9%
28%
28
72
Year of Reference Data No Information
56
10
34
Yes No No Information
Independent source of validation
Source of reference data
Graduate School of Geography, Clark University 12
Analysis
Error Matrix61%
Kappa 3%
No Matrix
Visual Estimate10%
5%
No Information12%
Only Overall Accuracy
9%
Graduate School of Geography, Clark University 13
Conclusions
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1. Most of the studies do not mention the sampling method2. Both points and polygons are used as sampling units3. 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 datasets5. Majority of the studies separate validation data from
classification data6. 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
Recommendations:
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• 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)
• 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
Graduate School of Geography, Clark University
• 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
Acknowledgements
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.
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