![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](https://image.slidesharecdn.com/recent-advances-in-object-based-change-detection1-110725163012-phpapp01/85/Recent-Advances-in-Object-based-Change-Detection-pdf-5-320.jpg)
Recent Advances in Object-based Change Detection.pdf
- 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 T1T2 T2T1 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/ Slide 26