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 L. Building Change Detection in Multitemporal Very High Resolution SAR Images[J].IEEE Transactions on Geoscience and Remote Sensing, , 2015, 53(5): 2664-2682. 坦克的仿真

Problems Urban evolution

Problems Earthquake damages

Outline Background Related Work Methodology Experimental Results Discussions

Outline Background Related Work Methodology Experimental Results Discussions

Background Challenges More heterogeneous Speckle

Background Challenges Geometric distortions ground layover Double bounce roof shadow

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

Outline Background Related Work Methodology Experimental Results Discussions

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

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

Outline Background Related Work Methodology Experimental Results Discussions

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

Methodology Backscattering properties of single images Single detected VHR SAR images

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

Methodology Backscattering properties of single images Single detected VHR SAR images

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

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

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

Methodology Log-ratio transform

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

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

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

Methodology Split-based threshold decision

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

Methodology Change building candidates

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

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

Methodology Fuzzy rules decision

Methodology Fuzzy rules decision Equivalence of length Alignment

Outline Background Related Work Methodology Experimental Results Discussions

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°

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

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

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°

Experimental Results Trento Data Set: Detection of New buildings

Experimental Results Trento Data Set: Detection of New buildings

Outline Background Related Work Methodology Experimental Results Discussions

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

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.