Generative Artificial Intelligence: How generative AI works.pdf
Segmentation scale selection
1. A method to select image
segmentation scale
Rahul Rakshit
rrakshit@clarku.edu
Graduate School of Geography
Clark University
hero.clarku.edu/holmes
Graduate School of Geography, Clark University 1
2. Scale Factor: heterogeneity of a segment increases with the increase of scale factor
30
Over-segmented
70 250
Under-segmented
Graduate School of Geography, Clark University 2
3. Segmentation Algorithms
Chessboard
Quadtree
Region Growing
Watershed
Graduate School of Geography, Clark University 3
4. Objective: Selection of suitable segmentation algorithm and scale
WS10 WS20 WS30
WS40 RG40 RG60
RG80 RG100 RG120
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5. Data
3 Bands, 15 cm, 1331x929, Aerial Photo, 2008, MassGIS
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6. Reference Dataset
Hand Digitized Segments, n= 546
Graduate School of Geography, Clark University 6
7. Segmentation scale comparison: Number of Segments
Under Segmented
RG120 RG= Region Growing
WS= Watershed
RG100
RG80
RG60
RG40
WS40
WS30
WS20
Over Segmented
WS10
Reference
0 1000 2000 3000 4000 5000 6000 7000
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8. Comparison Parameters
1. Circularity
2. Shape Index
3. Over Segmentation
4. Under Segmentation
5. Closeness
6. Hammoude Metric
7. Boundary Matching
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9. Comparing shape of segments: Circularity
A 2D geometric tolerance that controls how much a feature can deviate from a perfect circle
RG120
RG100
RG80
RG60
RG= Region Growing
WS= Watershed
RG40
WS40
WS30
WS20
WS10
Reference
0 5 10
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10. Comparing shape of segments: Geometric Feature Shape Index
perimeter
shapeindex (Neubert et al. 2008)
4 area
RG120
RG100
RG80
RG60
RG40
WS40
WS30
WS20
RG= Region Growing
WS10 WS= Watershed
Reference
0 1 2 3 4
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11. Over and Under Segmentation
oversegmentation2 undersegmentation2
Closeness (Clinton et al. 2010)
2
RG120
Over segmentation
RG100
Under segmentation
RG80
Closeness
RG60
RG40
WS40
WS30
WS20
WS10
0 0.5 1 1.5 2
Perfect Match Dissimilar
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12. Hammoude Metric
area(a b) area(a b)
H (Marcel 2009)
area(a b)
RG120
RG100
RG80
RG60
RG40
WS40
WS30
WS20
WS10
0.75 0.8 0.85 0.9 0.95 1
Dissimilar
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13. Distance Metric: Boundary Matching
D (r ) r= Boundary pixel of a segment in the reference map
D(r)= Euclidean distance between r and any boundary pixel in the segmented map
N N= Number of boundary pixels in the reference segment
(Delves et. al 1992)
RG120
RG100
RG80
RG60
RG40
WS40
WS30
WS20
WS10
0 0.5 1 1.5
Perfect Match Dissimilar
Graduate School of Geography, Clark University 13
14. Selection by weighted combination
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
•Average of the parameter is used
•Complement of the parameter (1-parameter) is used
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15. Results
WS20 Reference WS30
WS10
WS40 RG40 RG60
RG80 RG100 RG120
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17. Acknowledgements
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: 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 17
Notes de l'éditeur
I work in the holmes project in clark university and we produce land-cover maps of NE Massachusetts using very high resolution imagery. We use object based image analysis techniques to make these land-cover maps. There are many commercial image segmentation softwares available and each use different algorithms to segment the imagery. OBIA consists of two stages image segmentation and then classification of the segments. Segmentation scale is the most important parameter in the segmentation stage. UVM who gave us the training to use OBIA stated that a scale of 50 works best for them. But their imagery was different resolution from what we use for land-cover mapping. Even the literature on OBIA does not provide any information on the relation between resolution the image and the scale parameter.In our mapping exercise we had a choice of different segmentation algorithms and a choice of scale for each algorithm. The state of art in the choice of scale parameter now is to do eyeballing or visual interpretation. In this presentation I’ll show a method to quantify the selection of appropriate scale of segmentation.
The scale factor which is a unit-less parameter related to resolution of the image is an important characteristic of the segmentation procedure.The heterogeneity of a segment increases with the increase in the scale factor, thus larger scale factors tend to produce larger segments.In this image we want to map the land-cover of the baseball diamond. Fine green areas, impervious surfaces and bare soil are present here.In this segmented image I have used the scale of 30 and as we can see there are far more segments in the image than are necessary. This kind od segmentation is called oversegmentation.In this image the scale is 250 and the number of images is fewer than required and as we can see the segments are missing some patches of bare soil here and here. This kind of segmentation is called under segmentation.This image shows a segmentation of 70 and this is not that oversegmented as compared to scale 30 but still separates the 3 land-cover classes here.Oversegmentation is acceptable as we can always merge the segments after classification but too many segments can slow the process.Undersegmentation is undesirable as is not possible to divide the segments into finer segments during the classification process.So Over segmentation is ok but under segmentation is undesirable in the segmentation process.The scale factor is a relative term used in different softwares a scale of 10 in one software is not the same in some other software.
Watershed Algorithm: The image is regarded as a topographic surface with the gray values converted into gradients. The image is then is divided into a set of high-gradient watershed lines and low-gradient region interiors that act such as catchment basins. These catchment basins correspond to relatively homogeneous segments in the image.Region Growing : This algorithm aggregates pixels starting with seed points and grows into segments through a pair-wise clustering process until a certain threshold is reached which is normally a homogeneity criterion based on color, smoothness and compactness.
No information available on relation between pixel size and scale parameter. When compared to the reference segments, a better segmentation algorithm is the one which produces equal number of segments in the same locations. Selection of optimal algorithm-associated scale parameter is therefore an important step towards high quality segmentation for a given feature type
The segments in map b that have their centroid and at least 50% of surface area in the corresponding segment in map a are selected.
A circular segment would have the value of 1, and as the patch became more convoluted in shape, its shape index would increase in value.