Recent Advances in Object-based Change Detection

Transcription

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(m 1 = a 1T U - b 1T V) max under the constraint that var(a 1T U) = var(b 1T V) = 1 Further orthogonal variates m i can be computed Σm i 2 ~ Chi 2 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 I 1 and I 2 as stack segmentation not adequate for I 1 and I 2 Time 1 Time 2 Segmentation levels shape features cannot be used Image data Use segmentation of I 1 for I 2 Time 1 segmentation not adequate for I 2 Time 2 shape features cannot be used Independent segmentation leads to false-alarm segment changes shape features can be used 2. Segmentation Time 1 Time 2 Image data Image data Segmentation levels Segmentation levels 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 ecognition TM Starts with chessboard segmentation Selects iteratively a segment X and merges it to a neighboring segment Y if dxy (,) min ((,)) dxz Z NX () dyx (, ) min ((,)) dyz Z NY () d( Y, X) T 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 I 1 using multiresolution segmentation 2. Apply this segmentation to I 2 and recalculate color heterogeneity 3. Check each merge for consistency with I 2 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 I 2 as an initial segmentation 2. Segmentation Slide 11

12 Multiresolution segmentation adapted for object-based change detection 2 Given segment S 3 with children S 1 (seed) and S 2 Threshold test h(s 3 ) T check in I 2? Local best fitting test Is S 2 the locally best fitting neighbor for S 1 in I 2? Local mutual best fitting test Are S 1 and S 2 local mutually best fitting in I 2? Reduce sensitivity of the best fitting tests by using T checktolerance 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 objectbased change detection Directed i x i x = f S, n 1 y = f T n i y k k= 1 Via intersection x = f S, i y = f i x y 1 S 2 3. Object correspondence Slide 17

18 Object-based change detection Pre-processing Image-to-image registration, Radiometric normalization Canty & Nielsen 2009 Multiresolution segmentation adapted to change detection Segmentation e.g. Listner & Niemeyer 2010, 2011a, 2011b IR-MAD Change detection Nielsen 2007, Listner & Niemeyer 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 objects. Change detection using MAD objects. 4. Experiments Slide 22

23 Object-based change detection Accuracy assessment Overall accuracy Directed change detection: T1 T2 Directed change detection: T2 T1 Change detection using intersected objects Change detection using MAD objects KIA 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 objectbased 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 Jülich Germany Phone / Fax: / i.niemeyer@fz-juelich.de Slide 26