1. Building Change Detection in a
Couple of Optical and SAR High
Resolution Images
E. Barthelet1,2 G. Mercier1 L. Denise2
1 Telecom Bretagne, Dpt. Image and Signal Processing
2 Thales Communications & Security, Dpt. Image Intelligence
July 27, 2011

2. Presentation outline
Introduction Building projection Building extraction Performance Conclusion Bibliography
1 Introduction
2 Building projection in optical and SAR images
3 Building extraction method
4 Building extraction performance
5 Conclusion and further work
6 Bibliography
page 2 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

3. Presentation outline
Introduction Building projection Building extraction Performance Conclusion Bibliography
1 Introduction
2 Building projection in optical and SAR images
3 Building extraction method
4 Building extraction performance
5 Conclusion and further work
6 Bibliography
page 3 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

4. Introduction
Introduction Building projection Building extraction Performance Conclusion Bibliography
Building-oriented change detection
Available data:
• t1 : Georeferenced Optical image (Quickbird)
• t2 : Georeferenced SAR image (TerraSAR-X)
• Digital Elevation Model (DTED2)
Heterogeneous images:
• Different viewing angles
• Different illumination & weather conditions
• Different sensor types (optical & SAR)
page 4 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

5. Introduction
Introduction Building projection Building extraction Performance Conclusion Bibliography
Building-oriented change detection
Available data:
• t1 : Georeferenced Optical image (Quickbird)
• t2 : Georeferenced SAR image (TerraSAR-X)
• Digital Elevation Model (DTED2)
Heterogeneous images:
• Different viewing angles
• Different illumination & weather conditions
• Different sensor types (optical & SAR)
page 4 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

6. Introduction
Introduction Building projection Building extraction Performance Conclusion Bibliography
Building-oriented change detection
Optical Image (Quickbird) SAR Image (TerraSAR-X)
c DigitalGlobe
c Infoterra
A challenging problem !
page 5 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

7. Introduction
Introduction Building projection Building extraction Performance Conclusion Bibliography
Building-oriented change detection
Related work:
• P OULAIN et al., 2009 : building change detection between a
database and a pair of optical and SAR HR images
• B ENEDEK et al., 2009 : building change detection in a pair
of HR optical images
• B RUNNER et al., 2010 : building damage assessment from
optical pre-event and SAR post-event HR images
Proposed approach:
• Building extraction: hypothesis
generation-optimization-decision scheme
• Change decision: relies on extracted object consistancy
page 6 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

8. Introduction
Introduction Building projection Building extraction Performance Conclusion Bibliography
Building-oriented change detection
Related work:
• P OULAIN et al., 2009 : building change detection between a
database and a pair of optical and SAR HR images
• B ENEDEK et al., 2009 : building change detection in a pair
of HR optical images
• B RUNNER et al., 2010 : building damage assessment from
optical pre-event and SAR post-event HR images
Proposed approach:
• Building extraction: hypothesis
generation-optimization-decision scheme
• Change decision: relies on extracted object consistancy
page 6 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

9. Presentation outline
Introduction Building projection Building extraction Performance Conclusion Bibliography
1 Introduction
2 Building projection in optical and SAR images
3 Building extraction method
4 Building extraction performance
5 Conclusion and further work
6 Bibliography
page 7 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

10. Building projection in optical and SAR
images
Introduction Building projection Building extraction Performance Conclusion Bibliography
3D building model
Rectangular
parallelepiped
Flat roof
l w
h
N
α (xR , yR )
page 8 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

11. Building projection in optical and SAR
images
Introduction Building projection Building extraction Performance Conclusion Bibliography
3D building model Projection
Rectangular 3D physical or
parallelepiped empirical sensor
Flat roof model
Image metadata
l w DEM & Geoïd
h
N
α (xR , yR )
page 8 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

12. Building projection in optical and SAR
images
Introduction Building projection Building extraction Performance Conclusion Bibliography
3D building model Projection Optical signature
Rectangular 3D physical or
parallelepiped empirical sensor
Flat roof model
c DigitalGlobe
Image metadata
l w DEM & Geoïd
Roof
h Building fronts
N
Shadow
α (xR , yR )
page 8 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

13. Building projection in optical and SAR
images
Introduction Building projection Building extraction Performance Conclusion Bibliography
3D building model Projection SAR signature
Rectangular 3D physical or
parallelepiped empirical sensor
Flat roof model
c Infoterra
Image metadata
l w DEM & Geoïd
Layover
h Building fronts
N
Double bounce
α (xR , yR ) Shadow
page 8 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

14. Presentation outline
Introduction Building projection Building extraction Performance Conclusion Bibliography
1 Introduction
2 Building projection in optical and SAR images
3 Building extraction method
4 Building extraction performance
5 Conclusion and further work
6 Bibliography
page 9 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

15. Hypothesis adequacy criteria
Introduction Building projection Building extraction Performance Conclusion Bibliography
How to quantify the adequacy between a building hypothesis
and an image it has been projected into ?
page 10 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

16. Hypothesis adequacy criteria
Introduction Building projection Building extraction Performance Conclusion Bibliography
How to quantify the adequacy between a building hypothesis
and an image it has been projected into ?
Notations
Signature of building b in image I:
PI (b) = {Ri , i = 1 . . . N} = Sj , j = 1 . . . M
Set of regions
c DigitalGlobe Set of segments
c Infoterra
page 10 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

17. Hypothesis adequacy criteria
Introduction Building projection Building extraction Performance Conclusion Bibliography
First criterion Cregion
Statistical region-based approach (S PORTOUCHE et al.,
2009 )
Quantifies the homogeneity of signature regions
Generalized log-likelihood of region Ri :
ℓi = ℓ(Ri |Ωi ) = k ∈Ri ln p I(k)|Ωi
N
Cregion (PI (b)) = ℓ0 + i=1 ℓi
c DigitalGlobe
c Infoterra
page 11 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

18. Hypothesis adequacy criteria
Introduction Building projection Building extraction Performance Conclusion Bibliography
Second criterion Cedge
Edge-based approach (TOUZI et al., 1988 )
Quantifies the adequacy between signature segments and
image edges
Ratio r (Sj ) or difference d (Sj ) of median radiometric
values in neighborhoods of segment Sj

M M
j=1 r (Sj ) (multiplicative noise)

Cedge (PI (b)) = M
M
j=1 d (Sj ) (additive noise)

c DigitalGlobe
c Infoterra
page 12 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

19. Criterion behavior in optical and SAR
images
Introduction Building projection Building extraction Performance Conclusion Bibliography
Criteria are defined on a 6-dimensional support
Unidimensional sections of criteria Cregion and Cedge
Optical image SAR image
1.2
Cregion Cregion
1 Cedge Cedge
Criterion value
0.8
0.6
0.4
0.2
0
5 10 15 20 25 30 5 10 15 20 25 30
Building length (meters) Building length (meters)
c DigitalGlobe
c Infoterra
page 13 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

20. Hypothesis
generation-optimization-decision scheme
Introduction Building projection Building extraction Performance Conclusion Bibliography
Proposed Building extraction approach
1 Hypothesis generation: building parameters randomly
drawn in user-predefined intervals (exept for α)
2 Hypothesis optimization: successive optimization of
criteria Cregion and Cedge in the 6-dimension parameter
space (gradient descent)
3 Steps 1 & 2 are iterated within a Monte-Carlo framework
4 Hypothesis decision-making: supervised thresholding of
criteria Cregion and Cedge
page 14 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

21. Hypothesis
generation-optimization-decision scheme
Introduction Building projection Building extraction Performance Conclusion Bibliography
Proposed Building extraction approach
1 Hypothesis generation: building parameters randomly
drawn in user-predefined intervals (exept for α)
2 Hypothesis optimization: successive optimization of
criteria Cregion and Cedge in the 6-dimension parameter
space (gradient descent)
3 Steps 1 & 2 are iterated within a Monte-Carlo framework
4 Hypothesis decision-making: supervised thresholding of
criteria Cregion and Cedge
page 14 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

22. Hypothesis
generation-optimization-decision scheme
Introduction Building projection Building extraction Performance Conclusion Bibliography
Proposed Building extraction approach
1 Hypothesis generation: building parameters randomly
drawn in user-predefined intervals (exept for α)
2 Hypothesis optimization: successive optimization of
criteria Cregion and Cedge in the 6-dimension parameter
space (gradient descent)
3 Steps 1 & 2 are iterated within a Monte-Carlo framework
4 Hypothesis decision-making: supervised thresholding of
criteria Cregion and Cedge
page 14 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

23. Hypothesis
generation-optimization-decision scheme
Introduction Building projection Building extraction Performance Conclusion Bibliography
Proposed Building extraction approach
1 Hypothesis generation: building parameters randomly
drawn in user-predefined intervals (exept for α)
2 Hypothesis optimization: successive optimization of
criteria Cregion and Cedge in the 6-dimension parameter
space (gradient descent)
3 Steps 1 & 2 are iterated within a Monte-Carlo framework
4 Hypothesis decision-making: supervised thresholding of
criteria Cregion and Cedge
page 14 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

24. Presentation outline
Introduction Building projection Building extraction Performance Conclusion Bibliography
1 Introduction
2 Building projection in optical and SAR images
3 Building extraction method
4 Building extraction performance
5 Conclusion and further work
6 Bibliography
page 15 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

25. Building extraction detection performance
Introduction Building projection Building extraction Performance Conclusion Bibliography
Cartographic referential divided into 5m x 5m cells
Extraction method applied in each cell (3 MC draws)
page 16 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

26. Building extraction detection performance
Introduction Building projection Building extraction Performance Conclusion Bibliography
Cartographic referential divided into 5m x 5m cells
Extraction method applied in each cell (3 MC draws)
Optical image
c DigitalGlobe
``` Actual Building No building
Detected
``` cell cell
`
Building cell 8 1
No building cell 1 2098
page 16 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

27. Building extraction detection performance
Introduction Building projection Building extraction Performance Conclusion Bibliography
Cartographic referential divided into 5m x 5m cells
Extraction method applied in each cell (3 MC draws)
Optical image SAR image
c DigitalGlobe
c Infoterra
``` Actual Building No building
``` Actual Building No building
Detected
``` cell cell Detected
``` cell cell
` `
Building cell 8 1 Building cell 6 5
No building cell 1 2098 No building cell 3 2094
page 16 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

28. Building extraction estimation performance
Introduction Building projection Building extraction Performance Conclusion Bibliography
Building parameter bias & standard deviation (100 draws)
Building parameters w (m) l (m) h (m)
Probable right values 20 20 21
Estimator bias Optical -0.2 0.8 -0.1 Optical image
SAR 0.8 -2.3 -1.7
c DigitalGlobe
Estimator standard Optical 0.6 1.5 0.5
deviation SAR 4.4 3.2 5.2
SAR image
c Infoterra
page 17 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

29. Building extraction estimation performance
Introduction Building projection Building extraction Performance Conclusion Bibliography
Building parameter bias & standard deviation (100 draws)
Building parameters w (m) l (m) h (m)
Probable right values 20 20 21
Estimator bias Optical -0.2 0.8 -0.1 Optical image
SAR 0.8 -2.3 -1.7
c DigitalGlobe
Estimator standard Optical 0.6 1.5 0.5
deviation SAR 4.4 3.2 5.2
Building parameter uniform random initialization SAR image
0 0 5m l 30m
xR − 2.5m xR xR + 2.5m
c Infoterra
0 0 5m w 30m
yR − 2.5m yR yR + 2.5m
α = α0 5m h 30m
page 17 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

30. Presentation outline
Introduction Building projection Building extraction Performance Conclusion Bibliography
1 Introduction
2 Building projection in optical and SAR images
3 Building extraction method
4 Building extraction performance
5 Conclusion and further work
6 Bibliography
page 18 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

31. Conclusion
Introduction Building projection Building extraction Performance Conclusion Bibliography
New approach for jointly estimating and detecting buildings in
both optical and SAR images
Relies on:
• Simple 3D building model
• 3D building projection in optical and SAR images
• Region-based and edge-based criterion implementation
• Building hypothesis generation-optimization-decision
scheme
Interesting preliminary results
Developement with the support of the Orfeo Toolbox
page 19 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

32. Conclusion
Introduction Building projection Building extraction Performance Conclusion Bibliography
New approach for jointly estimating and detecting buildings in
both optical and SAR images
Relies on:
• Simple 3D building model
• 3D building projection in optical and SAR images
• Region-based and edge-based criterion implementation
• Building hypothesis generation-optimization-decision
scheme
Interesting preliminary results
Developement with the support of the Orfeo Toolbox
page 19 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

33. Conclusion
Introduction Building projection Building extraction Performance Conclusion Bibliography
New approach for jointly estimating and detecting buildings in
both optical and SAR images
Relies on:
• Simple 3D building model
• 3D building projection in optical and SAR images
• Region-based and edge-based criterion implementation
• Building hypothesis generation-optimization-decision
scheme
Interesting preliminary results
Developement with the support of the Orfeo Toolbox
page 19 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

34. Further work
Introduction Building projection Building extraction Performance Conclusion Bibliography
New approach for jointly estimating and detecting buildings in
both optical and SAR images
Wrap the extraction method in a Marked Point Process
framework
Primitive extraction:
• Building presence probability map (avoiding an exhaustive
search of buildings in images)
• Initialization of building parameters (avoiding uniform
random initialization)
page 20 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

35. Further work
Introduction Building projection Building extraction Performance Conclusion Bibliography
New approach for jointly estimating and detecting buildings in
both optical and SAR images
Wrap the extraction method in a Marked Point Process
framework
Primitive extraction:
• Building presence probability map (avoiding an exhaustive
search of buildings in images)
• Initialization of building parameters (avoiding uniform
random initialization)
page 20 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

36. Presentation outline
Introduction Building projection Building extraction Performance Conclusion Bibliography
1 Introduction
2 Building projection in optical and SAR images
3 Building extraction method
4 Building extraction performance
5 Conclusion and further work
6 Bibliography
page 21 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images

37. Bibliography
Introduction Building projection Building extraction Performance Conclusion Bibliography
C. Benedek, X. Descombes, and J. Zerubia.
Building extraction and change detection in multitemporal remotely sensed images with multiple birth and
death dynamics.
In IEEE Workshop on Applications of Computer Vision, pages 1–6, 2009.
D. Brunner, L. Bruzzone, and G. Lemoine.
Change detection for earthquake damage assessment in built-up areas using very high resolution optical
and sar imagery.
In IEEE International Geoscience and Remote Sensing Symposium, pages 3210–3213, 2010.
V. Poulain, J. Inglada, M. Spigai, J.-Y. Tourneret, and P. Marthon.
Fusion of high resolution optical and SAR images with vector data bases for change detection.
In IEEE International Geoscience and Remote Sensing Symposium, volume 4, pages 956–959, 2009.
H. Sportouche, F. Tupin, and L. Denise.
Building extraction and 3D reconstruction in urban areas from high-resolution optical and SAR imagery.
In Joint Urban Remote Sensing Event, pages 1–11, 2009.
R. Touzi, A. Lopes, and P. Bousquet.
A statistical and geometrical edge detector for SAR images.
IEEE Transactions on Geoscience and Remote Sensing, 26(6):764–773, November 1988.
page 22 E. Barthelet, G. Mercier, L.Denise Building Change Detection in High Resolution Images