1. Mitglied der Helmholtz-Gemeinschaft
IGARSS 2011, Vancouver
Change Detection and Multitemporal Image Analysis I
Recent Advances in Object-based
Change Detection
July 25, 2011 | Irmgard Niemeyer, Clemens Listner
Nuclear Safeguards Group
Institute of Energy and Climate Research
IEK-6: Nuclear Waste Management and Reactor Safety
Forschungszentrum Jülich GmbH, Germany

2. Acknowledgments
German Support Programme for the
International Atomic Energy Agency (IAEA)
Project on satellite imagery analysis and photo
interpretation support“
EC FP7, Global Monitoring for Environment and
Security (GMES)
Current project G-MOSAIC
General R&D interests
Methodological developments, PhD thesis Listner
Slide 2

3. Recent Advances in Object-based
Change Detection
Slide 3

4. Very high spatial resolution optical
sensors (<1m): WorldView-2
Slide 4

5. Object-based change detection using
IR-MAD
 Iteratively Reweighted Multivariate Alteration Detection
(IR-MAD) [Nielsen 2007]
 Linear transformation of the feature space aimed to
enhance the change information in the difference image
 Modeling object’s feature vector as random vectors F
and G of length N
 Transformation of vectors to enhance relevant changes
 var(m1 = a1TU - b1TV) → max
under the constraint that var(a1TU) = var(b1TV) = 1
 Further orthogonal variates mi can be computed
 Σmi2 ~ Chi2 indicating change probability P(change)
 Iteration by weighting with 1- P(change)
 Additional step: Application of PCA to U and V
1. Introduction Slide 5

6. Object-based change detection using
IR-MAD
 Statistical pixel-based change
detection approaches provide
good results, but shows limits
due to …
• low number of spectral
channels or small spectral
range covered,
• image registration problems.
 Object-based change detection
looks promising, but …
• how to connect corresponding objects?
• how to carry out a reasonable segmentation for
this task?
1. Introduction Slide 6

7. Existing approaches to segmentation
for object-based change detection
 Segment I1 and I2 as stack Time 1
• segmentation not adequate for I1
Segmentation
and I2
Time 2
levels
Image data
• shape features cannot be used
 Use segmentation of I1 for I2 Time 1
• segmentation not adequate for I2 Time 2
Segmentation
• shape features cannot be used Image data
levels
 Independent segmentation
Time 1
• leads to false-alarm segment
changes Time 2
Segmentation
levels
• shape features can be used
Image data
2. Segmentation Slide 7

8. Multiresolution segmentation
 Region-based bottom-up approach to segmentation
 Each segment is a binary tree
(leafs=pixel, root=final segment)
 Implemented in eCognitionTM
 Starts with chessboard segmentation
 Selects iteratively a segment X and merges it to a
neighboring segment Y if
X  (( ,
d , ) min))
( Y d Z
X

Z ()
NX
Y  ((,
d, ) min))
(X dZ
Y

Z ()
NY
d( , X)T
Y
2. Segmentation Slide 8

9. Multiresolution segmentation
2. Segmentation Slide 9

10. Multiresolution segmentation applied to
slightly different images
Segmentation of identical images up to Gaussian noise (μ=0,σ=0.1) using
multiresolution segmentation
2. Segmentation Slide 10

11. Multiresolution segmentation adapted
for object-based change detection 1
1. Segment I1 using multiresolution segmentation
2. Apply this segmentation to I2 and recalculate color
heterogeneity
3. Check each merge for consistency with I2 using a
predefined test
4. Remove inconsistent segments using a predefined
removal strategy
5. Re-run the multiresolution segmentation using the so
gained segmentation of I2 as an initial segmentation
2. Segmentation Slide 11

12. Multiresolution segmentation adapted
for object-based change detection 2
 Given segment S3 with children S1 (seed) and S2
 Threshold test
• h(S3) ≤ Tcheck in I2 ?
 Local best fitting test
• Is S2 the locally best fitting neighbor for S1 in I2 ?
 Local mutual best fitting test
• Are S1 and S2 local mutually best fitting in I2 ?
 Reduce sensitivity of the best fitting tests by using
Tchecktolerance
2. Segmentation Slide 12

13. Segmentation for object-based
change detection
Threshold test & universal segment removal strategy
2. Segmentation Slide 13

14. Segmentation for object-based
change detection
Local mutual best fitting test & global segment removal
strategy
2. Segmentation Slide 14

15. Segmentation for object-based
change detection
Local best fitting test & local segment removal strategy
2. Segmentation Slide 15

16. Segmentation for object-based
change detection
Threshold test & universal segment removal strategy
2. Segmentation Slide 16

17. Object correspondence for object-
based change detection
Directed Via intersection
xi = f x  Si  , xi = f x  S1  ,
1 n yi = f y  S 2 
yi =  f y Tk 
n k=1
3. Object correspondence Slide 17

18. Object-based change detection
Pre-processing
Image-to-image registration,
Radiometric normalization Canty & Nielsen 2009
Segmentation
Multiresolution segmentation adapted e.g. Listner & Niemeyer 2010, 2011a,
to change detection 2011b
Change detection
Nielsen 2007, Listner & Niemeyer
IR-MAD
2011b
Change classification
Class-based FFN Marpu 2009
Post-processing
Integration to GIS or GDBS
4. Experiments Slide 18

19. Object-based change detection
4. Experiments Slide 19

20. Object-based change detection
Segmentation of the bitemporal imagery using threshold test and
universal segment removal strategy.
4. Experiments Slide 20

21. Object-based change detection
Directed change detection. Changes from time 1 to time 2 (left) and
from time 2 to time 1 (right).
4. Experiments Slide 21

22. Object-based change detection
Change detection using intersected Change detection using MAD
objects. objects.
4. Experiments Slide 22

23. Object-based change detection
Accuracy assessment
Directed change Directed change Change Change
detection: detection: detection using detection using
T1T2 T2T1 intersected MAD objects
objects
Overall
0.98 0.98 0.98 0.99
accuracy
KIA 0.82 0.87 0.77 0.75
4. Experiments Slide 23

24. Summary
 An enhanced procedure for segmentation was
introduced and implemented into the change detection
workflow.
 Moreover, numerically issues in the IR-MAD method
were addressed.
 The proposed methods showed good results in three
experiments using aerial imagery.
 Further developments are needed:
• New consistency tests and segment removal
strategies;
• methods for enabling the user to easily select the
segmentation parameters, e.g. by using training
samples;
• implementation as eCognition plugin.
5. Summary Slide 24

25. Most recent publications
 C. Listner and I. Niemeyer (2011a), “Advances in object-
based change detection,” Proc. IGARSS 2011, Vancouver,
July 2011
 C. Listner and I. Niemeyer (2011b), “Object-based
change detection,” Photogrammetrie, Fernerkundung,
Geoinformation (PFG), vol. 3, 2011 (in print)
Slide 25

26. Thank you for your attention.
Dr. Irmgard Niemeyer
Nuclear Safeguards
Institute of Energy and Climate Research
IEK-6: Nuclear Waste Management and Reactor Safety
Forschungszentrum Jülich GmbH
in der Helmholtz-Gemeinschaft | 52425 Jülich | Germany
Phone / Fax: +49 2461 61-1762 / -2450
Email: i.niemeyer@fz-juelich.de
www.fz-juelich.de/ief/iek-6/
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