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Recent Advances in Object-based Change Detection.pdf

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  • 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. AcknowledgmentsGerman Support Programme for theInternational Atomic Energy Agency (IAEA)Project on satellite imagery analysis and photointerpretation support“EC FP7, Global Monitoring for Environment andSecurity (GMES)Current project G-MOSAICGeneral R&D interestsMethodological developments, PhD thesis Listner Slide 2
  • 3. Recent Advances in Object-basedChange Detection Slide 3
  • 4. Very high spatial resolution opticalsensors (<1m): WorldView-2 Slide 4
  • 5. Object-based change detection usingIR-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 V1. Introduction Slide 5
  • 6. Object-based change detection usingIR-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 segmentationfor 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 data2. 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 Y2. Segmentation Slide 8
  • 9. Multiresolution segmentation2. Segmentation Slide 9
  • 10. Multiresolution segmentation applied toslightly different imagesSegmentation of identical images up to Gaussian noise (μ=0,σ=0.1) usingmultiresolution segmentation2. Segmentation Slide 10
  • 11. Multiresolution segmentation adaptedfor object-based change detection 11. Segment I1 using multiresolution segmentation2. Apply this segmentation to I2 and recalculate color heterogeneity3. Check each merge for consistency with I2 using a predefined test4. Remove inconsistent segments using a predefined removal strategy5. Re-run the multiresolution segmentation using the so gained segmentation of I2 as an initial segmentation2. Segmentation Slide 11
  • 12. Multiresolution segmentation adaptedfor 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 Tchecktolerance2. Segmentation Slide 12
  • 13. Segmentation for object-basedchange detectionThreshold test & universal segment removal strategy2. Segmentation Slide 13
  • 14. Segmentation for object-basedchange detectionLocal mutual best fitting test & global segment removalstrategy2. Segmentation Slide 14
  • 15. Segmentation for object-basedchange detectionLocal best fitting test & local segment removal strategy2. Segmentation Slide 15
  • 16. Segmentation for object-basedchange detectionThreshold test & universal segment removal strategy2. 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=13. 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 GDBS4. Experiments Slide 18
  • 19. Object-based change detection4. Experiments Slide 19
  • 20. Object-based change detectionSegmentation of the bitemporal imagery using threshold test anduniversal segment removal strategy.4. Experiments Slide 20
  • 21. Object-based change detectionDirected change detection. Changes from time 1 to time 2 (left) andfrom time 2 to time 1 (right).4. Experiments Slide 21
  • 22. Object-based change detectionChange detection using intersected Change detection using MAD objects. objects.4. Experiments Slide 22
  • 23. Object-based change detectionAccuracy assessment Directed change Directed change Change Change detection: detection: detection using detection using T1T2 T2T1 intersected MAD objects objectsOverall 0.98 0.98 0.98 0.99accuracyKIA 0.82 0.87 0.77 0.754. 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

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