segmentation scale selection
DESCRIPTION
Presented in AAG 2010, Washington DCTRANSCRIPT
Graduate School of Geography, Clark University
Rahul [email protected] School of GeographyClark University
hero.clarku.edu/holmes
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segmentation scaleA method to select image
Graduate School of Geography, Clark University
Scale Factor: heterogeneity of a segment increases with the increase of scale factor
70
30
Over-
segm
ente
d
250
Under-
segm
ente
d
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Graduate School of Geography, Clark University
Chess
board
Quadtr
ee
Wate
rshed
Regio
n G
row
ing
Segmentation Algorithms
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Graduate School of Geography, Clark University
Objective: Selection of suitable segmentation algorithm and scale
RG120RG100RG80
RG40 RG60
WS10
WS40
WS30WS20WS10
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Graduate School of Geography, Clark University
Data
3 Bands, 15 cm, 1331x929, Aerial Photo, 2008, MassGIS
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Graduate School of Geography, Clark University
Reference Dataset
Hand Digitized Segments, n= 546
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Graduate School of Geography, Clark University
Segmentation scale comparison: Number of Segments
Reference
WS10
WS20
WS30
WS40
RG40
RG60
RG80
RG100
RG120
0 1000 2000 3000 4000 5000 6000 7000
RG= Region GrowingWS= Watershed
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Over Segmented
Under Segmented
Graduate School of Geography, Clark University
Comparison Parameters
1. Circularity2. Shape Index3. Over Segmentation4. Under Segmentation5. Closeness6. Hammoude Metric7. Boundary Matching
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Graduate School of Geography, Clark University
Comparing shape of segments: Circularity
Reference
WS10
WS20
WS30
WS40
RG40
RG60
RG80
RG100
RG120
0 5 10
RG= Region GrowingWS= Watershed
A 2D geometric tolerance that controls how much a feature can deviate from a perfect circle
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Graduate School of Geography, Clark University
Comparing shape of segments: Geometric Feature Shape Index
Reference
WS10
WS20
WS30
WS40
RG40
RG60
RG80
RG100
RG120
0 1 2 3 4
RG= Region GrowingWS= Watershed
area
perimetershapeindex
4 (Neubert et al. 2008)
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Graduate School of Geography, Clark University
Over and Under Segmentation
WS10
WS20
WS30
WS40
RG40
RG60
RG80
RG100
RG120
0 0.5 1 1.5 2
(Clinton et al. 2010)2
22 ntationundersegmetationoversegmenCloseness
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Over segmentation
Under segmentation
Closeness
DissimilarPerfect Match
Graduate School of Geography, Clark University
Hammoude Metric
WS10
WS20
WS30
WS40
RG40
RG60
RG80
RG100
RG120
0.75 0.8 0.85 0.9 0.95 1
)(
)()(
baarea
baareabaareaH
(Marcel 2009)
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Dissimilar
Graduate School of Geography, Clark University
Distance Metric: Boundary Matching
WS10
WS20
WS30
WS40
RG40
RG60
RG80
RG100
RG120
0 0.5 1 1.5
N
rD )( r= Boundary pixel of a segment in the reference mapD(r)= Euclidean distance between r and any boundary pixel in the segmented mapN= Number of boundary pixels in the reference segment
(Delves et. al 1992)
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DissimilarPerfect Match
Selection by weighted combination
Graduate School of Geography, Clark University
Comparison Parameter Weights
Difference in Circularity 2
Difference in Shape Index 2
Under Segmentation 1
Over Segmentation 5
Closeness 25
Hammoude Metric 25
Boundary Matching 40
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•Average of the parameter is used•Complement of the parameter (1-parameter) is used
Graduate School of Geography, Clark University
Results
RG80
WS40
WS10 WS30WS20
RG120RG100
RG40 RG60
Reference
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Graduate School of Geography, Clark University
Compare Segments tool
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Graduate School of Geography, Clark University
Advisors: Prof. Robert Gilmore Pontius, Jr. Prof. Colin Polsky
holmes Team: Albert Decatur Nick Giner Dan Runfola
Data: MassGIS
Software Support: James Toledano, IDRISI, Clark Labs Shitij Mehta, ESRI
More Information: [email protected] 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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