2D_Change_Detection_from_satellite_imagery_nchampion.FINAL.pdf

General and speciﬁc contexts
Method description and Evaluation Criteria
Experiments
Conclusion

2D Change Detection from satellite imagery :
Performance analysis and Impact of the spatial
resolution of input images

Nicolas Champion∗ , Didier Boldo, Marc Pierrot-Deseilligny, Georges Stamon

July 26th , 2011

1/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery

Method description and Evaluation Criteria General context
Experiments Speciﬁc context
Conclusion

General Context

General Issue
Updating maps → a crucial and topical issue in
most western countries


Conclusion

General Context

General Issue

General trends
to use remote-sensed images to perform the
detection of changes

to start the procedure from satellite images
Quickbird (Bailloeul et al., 2005)
Pl´iades-HR (Poulain et al., 2009)
e


Conclusion

General Context

General Issue

General trends

e

Some questions remain unanswered. . .
What is the optimal Ground Sample Distance (GSD) to use for input images ?


Conclusion

General Context

General Issue

General trends

e

The main goal of this presentation is provide possible answers to this question


Conclusion

General Context
General Issue

General trends

e

The main goal of this presentation is provide possible answers to this question
Methodology used for this paper → Evaluating the method by (Champion et al., 2009) 1
using 3 test areas and 2 diﬀerent GSD for input images

1. N. Champion, D. Boldo, M. Pierrot-Deseilligny, and G. Stamon. 2D building change detection from high resolution satellite
imagery : A two-step hierarchical method based on 3D invariant primitives. In : Pattern Recognition Letters, vol. 31, pp. 1138–1147, 2010.

Conclusion

Outline

1 General and speciﬁc contexts

2 Method description and Evaluation Criteria

3 Experiments

4 Conclusion


Conclusion

Speciﬁc context

Updating the French topographic vector
database (DB)


Conclusion

Speciﬁc context

database (DB)
changes to detect


Conclusion

Speciﬁc context

database (DB)
changes to detect
→ Demolished


Conclusion

Speciﬁc context

database (DB)
changes to detect
→ Demolished / Modiﬁed - Errors in the DB


Conclusion

Speciﬁc context

database (DB)
changes to detect
→ Demolished / Modiﬁed / New


Conclusion

Speciﬁc context

database (DB)
changes to detect
Images used to perform change detection
→ Pl´iades-HR ( c CNES)
e
a tri-stereosocpic system
4 channels → RGB + NIR (Near InfraRed)
2 datasets → GSD=50cm and GSD=70cm


Conclusion

Speciﬁc context

database (DB)
changes to detect

e

Products derived from input images
DSM (processed with MICMAC 1 )

1 - www.micmac.ign.fr

Conclusion

Speciﬁc context

database (DB)
changes to detect

e

RGB + NIR Orthophotos


Conclusion

Speciﬁc context

database (DB)
changes to detect

e

RGB + NIR Orthophotos
vegetation mask (NDVI)


Method Workﬂow : Step 1/Step 2
Method Description : Step 1
Experiments
Evaluation criteria
Conclusion

Outline

2 Method description and Evaluation Criteria
Evaluation criteria


Experiments
Evaluation criteria
Conclusion

Method Workﬂow

Input
Data
Database (DB)
to update

satellite
images

DSM

Orthophotos


Experiments
Evaluation criteria
Conclusion

Method Workﬂow

Input
Data
Database (DB)
to update

satellite
images nDSM

DSM DTM

Orthophotos


Experiments
Evaluation criteria
Conclusion

Method Workﬂow

Input
Step 1
Data Matching
Database (DB)
to update
Robust
Primitives
satellite
images nDSM
Decision

DSM DTM
Database
partially
Orthophotos
updated
Identifying
non-changed buildings . . .
. . . and changed buildings


Experiments
Evaluation criteria
Conclusion

Method Workﬂow

Input
Step 1
Data Matching
Database (DB)
to update
Robust
Primitives
satellite
images nDSM
Decision

updated DSM DTM
DTM
Database
partially
Orthophotos
updated
nDSM Identifying


Experiments
Evaluation criteria
Conclusion

Method Workﬂow

Input
Step 1
Data Matching
Database (DB)
to update
Robust
Primitives
satellite
images nDSM
Decision

updated DSM DTM
DTM
Database
partially
Orthophotos
updated
nDSM Identifying

Overground Mask t Overground Mask t-1

Morphological
comparison
New Building
Step 2 detection


Experiments
Evaluation criteria
Conclusion

Deﬁnition

Deﬁnition
a Digital Terrain Model (DTM) = a digital representation of the ground surface

DSM

DTM


Experiments
Evaluation criteria
Conclusion

Method used for processing the DTM
230m

200m
DSM

Method described in (Champion et al., 2009)
N. Champion, D. Boldo, M. Pierrot-Deseilligny and G. Stamon. Automatic estimation of ﬁne terrain models from multiple high

resolution images. In : Proc. of the ICIP Conference, 2009.


Experiments
Evaluation criteria
Conclusion

230m

200m
DSM Building and Vegetation Mask




Experiments
Evaluation criteria
Conclusion

230m

200m
DSM Building and Vegetation Mask

DTM




Experiments
Evaluation criteria
Conclusion

Database Veriﬁcation (Step 1)

Matching
Database (DB)
to update
Robust
Primitives
satellite
images nDSM
Decision

updated DSM DTM
DTM
Database
partially
Orthophotos
updated
nDSM Identifying


Morphological
comparison
New Building
detection


Experiments
Evaluation criteria
Conclusion


Step 1
Matching
Database (DB)
to update
Robust
Primitives
satellite
images nDSM
Decision

updated DSM DTM
DTM
Database
partially
Orthophotos
updated
nDSM Identifying


Morphological
comparison
New Building
detection


Experiments
Evaluation criteria
Conclusion

Database Veriﬁcation (Step 1) : Robust Primitives

(1) Information about Overground
based on the nDSM = DSM – DTM

6m

0m

input DSM nDSM


Experiments
Evaluation criteria
Conclusion



(2) Linear Primitives : 2D Contours
extracted from the DSM (Deriche, 1987)


Experiments
Evaluation criteria
Conclusion


(2) Linear Primitives : 2D Contours
extracted from the DSM (Deriche, 1987)

(3) Linear Primitives : 3D Segments
Reconstructed from input multiscopic images (Taillandier and Deriche, 2002)


Experiments
Evaluation criteria
Conclusion


Step 1
Matching
Database (DB)
to update
Robust
Primitives
satellite
images nDSM
Decision

updated DSM DTM
DTM
Database
partially
Orthophotos
updated
nDSM Identifying


Morphological
comparison
New Building
detection


Experiments
Evaluation criteria
Conclusion

Database Veriﬁcation (Step 1) : Matching and Decision
based on the a contrario paradigm 1
Dissimilarity Scores
nDSM Linear Primitives
nDSM → SDissimilarity Linear Primitives (2D contours & 3D segments) → SDissimilarity

1. A. Desolneux, L. Moisan, and J.-M. Morel, Meaningful alignments. In : International Journal of Computer Vision, 1 :40, 2000.

Experiments
Evaluation criteria
Conclusion

nDSM
Illustration of the computation of SDissimilarity


Experiments
Evaluation criteria
Conclusion

nDSM

nDSM


Experiments
Evaluation criteria
Conclusion

nDSM

Building to verify
nDSM


Experiments
Evaluation criteria
Conclusion

nDSM

Area
not covered
by overground pixel
in the nDSM

Building to verify
nDSM


Experiments
Evaluation criteria
Conclusion

nDSM

Area
not covered
by overground pixel
in the nDSM
Another area with
no coverage

Building to verify
nDSM


Experiments
Evaluation criteria
Conclusion

nDSM

Area
not covered
by overground pixel
in the nDSM
Another area with
no coverage

Building to verify
nDSM

nDSM
A (PBuilding (x,y) : nDSM(x,y) < TH )
SDissimilarity =
ABuilding


Experiments
Evaluation criteria
Conclusion


Decision Rules
Hypothesis : “a few changes in the scene” ⇒ “a building already present in
the database is likely still to be in the scene at the time of investigation.”


Experiments
Evaluation criteria
Conclusion


Decision Rules
⇒ A weak indication is enough to validate it during Step 1


Experiments
Evaluation criteria
Conclusion


Decision Rules
Linear Primitives nDSM
⇒ A small value for SDissimilarity or SDissimilarity


Experiments
Evaluation criteria
Conclusion


Decision Rules

{Unchanged Buildings} = Buildingi : SnDSM (Buildingi ) <
Dissimilarity M1
i∈[1,N]
∪
Linear Primitives
Buildingi : SDissimilarity (Buildingi ) < M2
i∈[1,N]


Experiments
Evaluation criteria
Conclusion


Decision Rules

{Unchanged Buildings} = Buildingi : SnDSM (Buildingi ) <
Dissimilarity M1
i∈[1,N]
∪
Linear Primitives
Buildingi : SDissimilarity (Buildingi ) < M2
i∈[1,N]

⇒ Our change detection system is actually a no-change detection system :
changes are inferred from {Unchanged Buildings} by taking the complementary


Experiments
Evaluation criteria
Conclusion

Method output - Illustration on Amiens Pont de Metz (GSD = 50cm)

Change Map Evaluation Map

unchanged modiﬁed demolished new TP FN FP TN


Experiments
Evaluation criteria
Conclusion

Evaluation Criteria

Success criteria used in this project (Champion et al., 2009) 1
Confusion Matrix
XX
XXX System (S)
Unchanged (S) M&D2 (S) New (S) Bg.3 (S)
Ref.1 (R)
XX
X X
Unchanged (R) # NU(S)→U(R) # NC(S)→U(R) - -

M&D2 (R) # NU(S)→C(R) # NC(S)→C(R) - -

New (R) - - # NN(S)→N(R) # NB(S)→N(R)

Bg.3 (R) - - # NN(S)→B(R) -

1
Ref. = Reference 2 M&D = Modiﬁed and Demolished 3 Bg. = Background

1. N. Champion, F. Rottensteiner, L. Matikainen, X. Liang, J. Hyyppa, and B. Olsen, “A test of automatic building change detection
approaches.,” in International Archives of Photogrammetry and Remote Sensing, Vol. 38, Part 3/W4, 2009.

Experiments
Evaluation criteria
Conclusion

Evaluation Criteria

Confusion Matrix
XX
XXX System (S)
Ref.1 (R)
XXX
X

M&D2 (R) # NU(S)→C(R) # NC(S)→C(R) - -

New (R) - - # NN(S)→N(R) # NB(S)→N(R)

Bg.3 (R) - - # NN(S)→B(R) -

Computing the Recall and Precision for each change class - e.g. :

1


Experiments
Evaluation criteria
Conclusion

Evaluation Criteria
Confusion Matrix
XXX
XXX (S)
System
Ref.1 (R) XX X

M&D2 (R) # NU(S)→C(R) # NC(S)→C(R) - -

New (R) - - # NN(S)→N(R) # NB(S)→N(R)

Bg.3 (R) - - # NN(S)→B(R) -


#NC(S)→C(R)
RecallM&D =
#NC(S)→C(R) + #NU(S)→C(R)

1


Experiments
Evaluation criteria
Conclusion

Evaluation Criteria
Confusion Matrix
XXX
XXX (S)
System
Ref.1 (R) XX X

M&D2 (R) # NU(S)→C(R) # NC(S)→C(R) - -

New (R) - - # NN(S)→N(R) # NB(S)→N(R)

Bg.3 (R) - - # NN(S)→B(R) -


#NC(S)→C(R) #NC(S)→C(R)
RecallM&D = / PrecisionM&D =
#NC(S)→C(R) + #NU(S)→C(R) #NC(S)→C(R) + #NC(S)→U(R)

1


Experiments
Evaluation criteria
Conclusion

Evaluation Criteria

Confusion Matrix
XX
XXX System (S)
Ref.1 (R)
XXX
X

M&D2 (R) # NU(S)→C(R) # NC(S)→C(R) - -

New (R) - - # NN(S)→N(R) # NB(S)→N(R)

Bg.3 (R) - - # NN(S)→B(R) -

Computing the Recall and Precision for each change class

1st success criterion (qualitative eﬀectiveness)
⇒ a high recall for all changes = Demolished & Modiﬁed and New

1


Experiments
Evaluation criteria
Conclusion

Evaluation Criteria
Confusion Matrix
XXX
XXX (S)
System
Ref.1 (R) XX X

M&D2 (R) # NU(S)→C(R) # NC(S)→C(R) - -

New (R) - - # NN(S)→N(R) # NB(S)→N(R)

Bg.3 (R) - - # NN(S)→B(R) -

Computing the Recall and Precision for each change class

1st success criterion (qualitative effectiveness)
⇒ a high recall for all changes = Demolished & Modified and New

2nd success criterion (economical effectiveness)
to limit the number of false alarms ⇒ a high precision for modified and demolished buildings
⇔ a high recall for unchanged buildings (a lower precision for new buildings is not critical)

1

Method description and Evaluation Criteria Evaluation at 50cm
Experiments Comparison 50cm/70cm
Conclusion

Evaluation at 50cm
Analysis

Quantitative Evaluation (50cm)
Unchanged M&D1 New
Amiens Downtown
Recall 95,7 95,45 80,3
Precision 99,8 48,09 28,49
Amiens Pont-de-Metz
Recall 84,98 96,55 92,13
Precision 98,57 69,65 38,86
Toulouse Rocade
Recall 80,52 96,99 76,47
Precision 99,55 37,61 18,44

1 - Modiﬁed and Demolished


Conclusion

Evaluation at 50cm
Analysis
Recall (Qualitative Eﬀectiveness)
Quantitative Evaluation (50cm) very good for modiﬁed and
demolished buildings ( 95%)
Unchanged M&D1 New
Amiens Downtown
Recall 95,7 95,45 80,3
Precision 99,8 48,09 28,49
Amiens Pont-de-Metz
Recall 84,98 96,55 92,13
Precision 98,57 69,65 38,86
Toulouse Rocade
Recall 80,52 96,99 76,47
Precision 99,55 37,61 18,44



Conclusion

Evaluation at 50cm
Analysis
Unchanged M&D1 New good for new buildings (on
Amiens Downtown average 85%) . . .
Recall 95,7 95,45 80,3
Precision 99,8 48,09 28,49
Amiens Pont-de-Metz
Recall 84,98 96,55 92,13
Precision 98,57 69,65 38,86
Toulouse Rocade
Recall 80,52 96,99 76,47
Precision 99,55 37,61 18,44



Conclusion

Evaluation at 50cm
Analysis
Amiens Downtown average 85%) . . . but sometimes
Recall 95,7 95,45 80,3 critical (< 80%)
Precision 99,8 48,09 28,49
Amiens Pont-de-Metz
Recall 84,98 96,55 92,13
Precision 98,57 69,65 38,86
Toulouse Rocade
Recall 80,52 96,99 76,47
Precision 99,55 37,61 18,44



Conclusion

Evaluation at 50cm
Analysis
Recall 95,7 95,45 80,3 critical (< 80%)
Precision 99,8 48,09 28,49
Precision (Economical Eﬀectiveness)
Amiens Pont-de-Metz
relatively low for new buildings
Recall 84,98 96,55 92,13
Precision 98,57 69,65 38,86
Toulouse Rocade
Recall 80,52 96,99 76,47
Precision 99,55 37,61 18,44



Conclusion

Evaluation at 50cm
Analysis
Recall 95,7 95,45 80,3 critical (< 80%)
Precision 99,8 48,09 28,49
Amiens Pont-de-Metz
Recall 84,98 96,55 92,13
→ < 20% in Toulouse Rocade
Precision 98,57 69,65 38,86
Toulouse Rocade
Recall 80,52 96,99 76,47
Precision 99,55 37,61 18,44



Conclusion

Evaluation at 50cm
Analysis
Recall 95,7 95,45 80,3 critical (< 80%)
Precision 99,8 48,09 28,49
Amiens Pont-de-Metz
Recall 84,98 96,55 92,13
Precision 98,57 69,65 38,86
better for demolished and
Toulouse Rocade
modiﬁed buildings (70% in
Recall 80,52 96,99 76,47
Amiens Pont-de-Metz)
Precision 99,55 37,61 18,44



Conclusion

Evaluation at 50cm
Analysis
Recall 95,7 95,45 80,3 critical (< 80%)
Precision 99,8 48,09 28,49
Amiens Pont-de-Metz
Recall 84,98 96,55 92,13
Precision 98,57 69,65 38,86
better for demolished and
Toulouse Rocade
modiﬁed buildings (70% in
Recall 80,52 96,99 76,47
Amiens Pont-de-Metz)
Precision 99,55 37,61 18,44
a high recall for unchanged buildings
→ work saved for operators


2D_Change_Detection_from_satellite_imagery_nchampion.FINAL.pdf

2D_Change_Detection_from_satellite_imagery_nchampion.FINAL.pdf

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