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Building Change Detection in Multitemporal Very High Resolution SAR Images L. Bruzzone, et al. Juanping Zhao 2015.07.17 [1] Marin C, Bovolo F, Bruzzone.

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Presentation on theme: "Building Change Detection in Multitemporal Very High Resolution SAR Images L. Bruzzone, et al. Juanping Zhao 2015.07.17 [1] Marin C, Bovolo F, Bruzzone."— Presentation transcript:

1 Building Change Detection in Multitemporal Very High Resolution SAR Images L. Bruzzone, et al.
Juanping Zhao [1] Marin C, Bovolo F, Bruzzone L. Building Change Detection in Multitemporal Very High Resolution SAR Images[J].IEEE Transactions on Geoscience and Remote Sensing, , 2015, 53(5): 坦克的仿真

2 Problems Urban evolution

3 Problems Earthquake damages

4 Outline Background Related Work Methodology Experimental Results
Discussions

5 Outline Background Related Work Methodology Experimental Results
Discussions

6 Background Challenges More heterogeneous Speckle

7 Background Challenges Geometric distortions ground layover
Double bounce roof shadow

8 Background Challenges Semantic Confusion
Intra-Class Variations (operation conditions)

9 Outline Background Related Work Methodology Experimental Results
Discussions

10 Urban areas Change detection
Related works Urban areas Change detection Earthquake damages Building database updating Urban evolution… Methods Supervised analysis Fusion Large geometric scale

11 Building CD using Double Bounce Line
Related works Building CD using Double Bounce Line Partial Information Double bounce line is not reliable

12 Outline Background Related Work Methodology Experimental Results
Discussions

13 Methodology Two basic ideas
Extract information of change at optimal building scale Exploit backscattering properties of bi-temporal images

14 Methodology Backscattering properties of single images
Single detected VHR SAR images

15 Methodology Backscattering properties of single images
Single detected VHR SAR images Backscattering properties of single images

16 Methodology Backscattering properties of single images
Single detected VHR SAR images

17 Bi-temporal VHR SAR images
Methodology Backscattering properties of bi-temporal images Bi-temporal VHR SAR images

18 Multiscale Decomposition
Methodology Step 1 Log-ratio Transform Step 2 Multiscale Decomposition Step 3 Split-based Analysis Step 4 Candidate Detection Step 5 Fuzzy Classification Architecture of the proposed approach to building CD

19 Architecture of the proposed approach to building CD
Methodology Log-ratio transform Architecture of the proposed approach to building CD

20 Methodology Log-ratio transform

21 Architecture of the proposed approach to building CD
Methodology Detection of backscattering changes at optimal building scale Architecture of the proposed approach to building CD

22 2D stationary wavelet transform
Methodology Detection of backscattering changes at optimal building scale 2D stationary wavelet transform Reducing the impact of small changes mitigation of the speckle effect

23 Architecture of the proposed approach to building CD
Methodology Split-based threshold decision Architecture of the proposed approach to building CD

24 Methodology Split-based threshold decision

25 Architecture of the proposed approach to building CD
Methodology Change building candidates Architecture of the proposed approach to building CD

26 Methodology Change building candidates

27 Architecture of the proposed approach to building CD
Methodology Fuzzy rules decision Architecture of the proposed approach to building CD

28 Methodology Four Classes Fuzzy rules decision
fully destroyed buildings new buildings changes that have a size comparable to a building all other changes that do not show a size comparable to a building Fuzzy rules decision Completeness Proportionality of areas Equivalence of length Alignment

29 Methodology Fuzzy rules decision

30 Methodology Fuzzy rules decision Equivalence of length Alignment

31 Outline Background Related Work Methodology Experimental Results
Discussions

32 2009 L’Aquila Earthquake: Detection of Destroyed buildings
Experimental Results 2009 L’Aquila Earthquake: Detection of Destroyed buildings sensor COSMO-SkyMed mode spotlight band X looks 1 resolution 1m Pixel spacing 0.5m*0.5m Pol-mode HH orbit ascending Incidence angle 57°- 58°

33 Experimental Results 2009 L’Aquila Earthquake: Detection of Destroyed buildings

34 Experimental Results 2009 L’Aquila Earthquake: Detection of Destroyed buildings

35 Trento Data Set: Detection of New buildings
Experimental Results Trento Data Set: Detection of New buildings sensor TerraSAR-X TanDEM-X mode spotlight band X波段 looks 1 resolution 0.58m*1.1m Pixel spacing 0.454m*0.855m Pol-mode HH orbit ascending Incidence angle 53°

36 Experimental Results Trento Data Set: Detection of New buildings

37 Experimental Results Trento Data Set: Detection of New buildings

38 Outline Background Related Work Methodology Experimental Results
Discussions

39 Discussions Taking home messages Unsupervised
Scattering properties of bi-temporal images Multitemporal correlation between images Intrinsic multiscale nature of objects present in VHR images More flexible

40 Possibilities to improve
Discussions Possibilities to improve Improve the building detector by better modeling the geometrical behaviors of building primitives; Investigate the possibility to discriminate among several building construction stages and/or building damage levels.

41

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