KIT – University of the State of Baden-Württemberg andNational Large-scale Research Center of the Helmholtz Association
1 IPF - Institute of Photogrammetry and Remote Sensing, Karlsruhe, Germany2 Fraunhofer FOM, Research Institute for Optronics and Pattern Recognition, Ettlingen, Germany
www.kit.edu
Fusion of InSAR and GIS data for 3D building reconstruction and change detection
Antje Thiele1,2, Stefan Hinz1, Erich Cadario2
IPF – Institute of Photogrammetryand Remote Sensing
2 03.12.2009 Fusion of InSAR and GIS data for 3D building reconstruction and change detection
Question: Why Building Reconstruction?
Answer: High relevance of 3D city models
Motivation
Visualization
source: virtualcitySYSTEMS
Mission planningSimulationUpdating
source: GRASS (M. Netele)
Change detectionDisaster managementUpdating of map informationDeformation monitoring
source: www.dvice.com
IPF – Institute of Photogrammetryand Remote Sensing
3 03.12.2009 Fusion of InSAR and GIS data for 3D building reconstruction and change detection
Rapid mapping (short-term applications)no PSIno Tomography
Preparation for TanDEM-X mission
Question: Why Building Reconstruction?
Answer: High relevance of 3D city models
Motivation
Visualization
Mission planningSimulationUpdating
Change detectionDisaster managementUpdating of map informationDeformation monitoring
IPF – Institute of Photogrammetryand Remote Sensing
4 03.12.2009 Fusion of InSAR and GIS data for 3D building reconstruction and change detection
layover
roofshadow
slant range
corner
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h1
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l1
Signature of isolated buildings:
SAR magnitude signature
Motivation
0 50 100 150 200 250Slant-Range [pixel]
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Phase-V
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layover area building area
Bui
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]
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0 50 100 150 200 250Slant-Range [pixel]
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Phase-V
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layover area building area
Bui
ldin
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t[m
]
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InSAR phase signature
layover
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slant range
corner
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h1
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l2
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layover
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slant range
corner
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h1
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layover
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slant range
corner
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h1
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l1
l2
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IPF – Institute of Photogrammetryand Remote Sensing
5 03.12.2009 Fusion of InSAR and GIS data for 3D building reconstruction and change detection
layoverroof
shadow
slant range
corner
�
layover
corner
shadow
roof
Motivation
Signature of in dense urban area:
SAR magnitude signature
layoverroof
shadow
slant range
corner
�
layover
corner
shadow
roof
IPF – Institute of Photogrammetryand Remote Sensing
6 03.12.2009 Fusion of InSAR and GIS data for 3D building reconstruction and change detection
layoverroof
shadow
slant range
corner
�
layover
corner
shadow
roof
Motivation
Signature of in dense urban area:
SAR magnitude signature
InSAR phase signature
IPF – Institute of Photogrammetryand Remote Sensing
7 03.12.2009 Fusion of InSAR and GIS data for 3D building reconstruction and change detection
layoverroof
shadow
slant range
corner
�
layover
corner
shadow
roof
Motivation
Signature of in dense urban area:
SAR magnitude signature
InSAR phase signature
IPF – Institute of Photogrammetryand Remote Sensing
8 03.12.2009 Fusion of InSAR and GIS data for 3D building reconstruction and change detection
Content
Approach of 3D building reconstruction
Current results of analyzing airborne InSAR data
Outlook
IPF – Institute of Photogrammetryand Remote Sensing
9 03.12.2009 Fusion of InSAR and GIS data for 3D building reconstruction and change detection
Approach of 3D building reconstruction
InSAR Data
-airborne e.g. STAR3i
-space-borne
e.g. TerraSAR-X
Simulation of InSAR Phases
e.g. simple model –
Thiele et al. 2007: “InSAR phase profiles at building locations“
Assessment of Phases
e.g. correlation coefficient
0 5 10 15 200
0.2
0.4
0.6
0.8
1
building height [m]
corre
latio
n va
lue
Visualization
e.g. optical image overlaid with 3D-
model
GIS-Information
e.g. cadastral information, reconstruction results
IPF – Institute of Photogrammetryand Remote Sensing
10 03.12.2009 Fusion of InSAR and GIS data for 3D building reconstruction and change detection
Approach of 3D building reconstruction
A1
A
B
C
r1A
r1B
r1C
r2A
r2Br2C
A2
equidistant ground spacing
equidistant range spacingA1
A
B
C
r1A
r1B
r1C
r2A
r2Br2C
A2
equidistant ground spacing
equidistant range spacing
Contribution distribution ofInSAR measurements at building location
Thiele, A., Cadario, E., Schulz, K., Thoennessen, U., Soergel, U. (2007): InSAR Phase Profiles at building Locations. In: Proceedings of ISPRS Photogrammetric
Image Analysis, Vol. XXXVI, Part 3/W49A, pp. 203-208.
IPF – Institute of Photogrammetryand Remote Sensing
11 03.12.2009 Fusion of InSAR and GIS data for 3D building reconstruction and change detection
Approach of 3D building reconstruction
A1
A
B
C
r1A
r1B
r1C
r2A
r2Br2C
A2
equidistant ground spacing
equidistant range spacingA1
A
B
C
r1A
r1B
r1C
r2A
r2Br2C
A2
equidistant ground spacing
equidistant range spacing
Contribution distribution ofInSAR measurements at building location
Implementation of Phase Mixture Model
A
r1A r1B r1C
DSM height profile
B
C
fragment 1
PS1 PE1
PS4
PE4
fragment 4αA
αB
αC
A
r1A r1B r1C
DSM height profile
B
C
fragment 1
PS1 PE1
PS4
PE4
fragment 4αA
αB
αC
32
Thiele, A., Cadario, E., Schulz, K., Thoennessen, U., Soergel, U. (2007): InSAR Phase Profiles at building Locations. In: Proceedings of ISPRS Photogrammetric
Image Analysis, Vol. XXXVI, Part 3/W49A, pp. 203-208.
IPF – Institute of Photogrammetryand Remote Sensing
12 03.12.2009 Fusion of InSAR and GIS data for 3D building reconstruction and change detection
Approach of 3D building reconstruction
Interferogram calculation:
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mjeam
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coscos
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22
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11
1(
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SSS
Modeling assumptions: considering only direct reflection
weighting backscatter magnitudes by local incidence angle
using generalized DSM
considering no material model information
neglecting shadow noise impact
IPF – Institute of Photogrammetryand Remote Sensing
13 03.12.2009 Fusion of InSAR and GIS data for 3D building reconstruction and change detection
Current results of analyzing airborne InSAR data
IPF – Institute of Photogrammetryand Remote Sensing
14 03.12.2009 Fusion of InSAR and GIS data for 3D building reconstruction and change detection
Current results of analyzing airborne InSAR data
IPF – Institute of Photogrammetryand Remote Sensing
15 03.12.2009 Fusion of InSAR and GIS data for 3D building reconstruction and change detection
Current results of analyzing airborne InSAR data
0 5 10 15 200
0.2
0.4
0.6
0.8
1
building height [m]
corre
latio
n va
lue
0 5 10 15 200
0.2
0.4
0.6
0.8
1
building height [m]
corre
latio
n va
lue
0 5 10 15 200
0.2
0.4
0.6
0.8
1
building height [m]
corre
latio
n va
lue
IPF – Institute of Photogrammetryand Remote Sensing
16 03.12.2009 Fusion of InSAR and GIS data for 3D building reconstruction and change detection
Outlook
Improvement of correlation result by:Modifying simulation (e.g. weighting of facades, considering shadow) Modifying interferogram calculation (e.g. smart filtering)
ml 3x3 pixel ml 9x9 pixel
M. Eineder, DLR
IPF – Institute of Photogrammetryand Remote Sensing
17 03.12.2009 Fusion of InSAR and GIS data for 3D building reconstruction and change detection
OutlookTheoretical evaluation for TanDEM-X
Height error of ca. 1 –
1,5m (also for moderate coherence)Carefully chosen baselines and incidence angles close to the limit of TanDEM-X
Reconstruction of dense urban scenes considering interaction effects
between neighbored building
source: Google
IPF – Institute of Photogrammetryand Remote Sensing
18 03.12.2009 Fusion of InSAR and GIS data for 3D building reconstruction and change detection
Outlook
Thank you for your attention!
Theoretical evaluation for TanDEM-XHeight error of ca. 1 –
1,5m (also for moderate coherence)Carefully chosen baselines and incidence angles close to the limit of TanDEM-X
Reconstruction of dense urban scenes considering interaction effects
between neighbored building
Extraction of Changes
IPF – Institute of Photogrammetryand Remote Sensing
19 03.12.2009 Fusion of InSAR and GIS data for 3D building reconstruction and change detection
Open PhD position at Karlsruhe Institute of Technology (KIT), Germany
Topic: Fusion of GPS-Tomography and SAR-Interferometry
for estimating atmospheric water vapor
Details: -
Position jointly hosted by Institute of Geodesy and Institute of Photogrammetry
and Remote Sensing
-
Full-time research position, 24 months, funded by research cluster „Hydrosphere“
Contact: -
Prof. Stefan Hinz
(KIT) → [email protected]
(for more information, see announcement at information desk)