2D_Change_Detection_from_satellite_imagery_nchampion.FINAL.pdf

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2D_Change_Detection_from_satellite_imagery_nchampion.FINAL.pdf

  1. 1. General and specific contexts Method description and Evaluation Criteria Experiments Conclusion 2D Change Detection from satellite imagery : Performance analysis and Impact of the spatial resolution of input images Nicolas Champion∗ , Didier Boldo, Marc Pierrot-Deseilligny, Georges Stamon July 26th , 20111/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  2. 2. General and specific contexts Method description and Evaluation Criteria General context Experiments Specific context Conclusion General Context General Issue Updating maps → a crucial and topical issue in most western countries2/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  3. 3. General and specific contexts Method description and Evaluation Criteria General context Experiments Specific context Conclusion General Context General Issue Updating maps → a crucial and topical issue in most western countries General trends to use remote-sensed images to perform the detection of changes to start the procedure from satellite images Quickbird (Bailloeul et al., 2005) Pl´iades-HR (Poulain et al., 2009) e2/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  4. 4. General and specific contexts Method description and Evaluation Criteria General context Experiments Specific context Conclusion General Context General Issue Updating maps → a crucial and topical issue in most western countries General trends to use remote-sensed images to perform the detection of changes to start the procedure from satellite images Quickbird (Bailloeul et al., 2005) Pl´iades-HR (Poulain et al., 2009) e Some questions remain unanswered. . . What is the optimal Ground Sample Distance (GSD) to use for input images ?2/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  5. 5. General and specific contexts Method description and Evaluation Criteria General context Experiments Specific context Conclusion General Context General Issue Updating maps → a crucial and topical issue in most western countries General trends to use remote-sensed images to perform the detection of changes to start the procedure from satellite images Quickbird (Bailloeul et al., 2005) Pl´iades-HR (Poulain et al., 2009) e Some questions remain unanswered. . . What is the optimal Ground Sample Distance (GSD) to use for input images ? The main goal of this presentation is provide possible answers to this question2/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  6. 6. General and specific contexts Method description and Evaluation Criteria General context Experiments Specific context Conclusion General Context General Issue Updating maps → a crucial and topical issue in most western countries General trends to use remote-sensed images to perform the detection of changes to start the procedure from satellite images Quickbird (Bailloeul et al., 2005) Pl´iades-HR (Poulain et al., 2009) e Some questions remain unanswered. . . What is the optimal Ground Sample Distance (GSD) to use for input images ? The main goal of this presentation is provide possible answers to this question Methodology used for this paper → Evaluating the method by (Champion et al., 2009) 1 using 3 test areas and 2 different GSD for input images 1. N. Champion, D. Boldo, M. Pierrot-Deseilligny, and G. Stamon. 2D building change detection from high resolution satellite imagery : A two-step hierarchical method based on 3D invariant primitives. In : Pattern Recognition Letters, vol. 31, pp. 1138–1147, 2010.2/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  7. 7. General and specific contexts Method description and Evaluation Criteria General context Experiments Specific context Conclusion Outline 1 General and specific contexts 2 Method description and Evaluation Criteria 3 Experiments 4 Conclusion3/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  8. 8. General and specific contexts Method description and Evaluation Criteria General context Experiments Specific context Conclusion Specific context Updating the French topographic vector database (DB)4/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  9. 9. General and specific contexts Method description and Evaluation Criteria General context Experiments Specific context Conclusion Specific context Updating the French topographic vector database (DB) changes to detect4/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  10. 10. General and specific contexts Method description and Evaluation Criteria General context Experiments Specific context Conclusion Specific context Updating the French topographic vector database (DB) changes to detect → Demolished4/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  11. 11. General and specific contexts Method description and Evaluation Criteria General context Experiments Specific context Conclusion Specific context Updating the French topographic vector database (DB) changes to detect → Demolished / Modified - Errors in the DB4/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  12. 12. General and specific contexts Method description and Evaluation Criteria General context Experiments Specific context Conclusion Specific context Updating the French topographic vector database (DB) changes to detect → Demolished / Modified / New4/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  13. 13. General and specific contexts Method description and Evaluation Criteria General context Experiments Specific context Conclusion Specific context Updating the French topographic vector database (DB) changes to detect → Demolished / Modified / New Images used to perform change detection → Pl´iades-HR ( c CNES) e a tri-stereosocpic system 4 channels → RGB + NIR (Near InfraRed) 2 datasets → GSD=50cm and GSD=70cm4/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  14. 14. General and specific contexts Method description and Evaluation Criteria General context Experiments Specific context Conclusion Specific context Updating the French topographic vector database (DB) changes to detect → Demolished / Modified / New Images used to perform change detection → Pl´iades-HR ( c CNES) e a tri-stereosocpic system 4 channels → RGB + NIR (Near InfraRed) 2 datasets → GSD=50cm and GSD=70cm Products derived from input images DSM (processed with MICMAC 1 ) 1 - www.micmac.ign.fr4/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  15. 15. General and specific contexts Method description and Evaluation Criteria General context Experiments Specific context Conclusion Specific context Updating the French topographic vector database (DB) changes to detect → Demolished / Modified / New Images used to perform change detection → Pl´iades-HR ( c CNES) e a tri-stereosocpic system 4 channels → RGB + NIR (Near InfraRed) 2 datasets → GSD=50cm and GSD=70cm Products derived from input images DSM (processed with MICMAC 1 ) RGB + NIR Orthophotos 1 - www.micmac.ign.fr4/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  16. 16. General and specific contexts Method description and Evaluation Criteria General context Experiments Specific context Conclusion Specific context Updating the French topographic vector database (DB) changes to detect → Demolished / Modified / New Images used to perform change detection → Pl´iades-HR ( c CNES) e a tri-stereosocpic system 4 channels → RGB + NIR (Near InfraRed) 2 datasets → GSD=50cm and GSD=70cm Products derived from input images DSM (processed with MICMAC 1 ) RGB + NIR Orthophotos vegetation mask (NDVI) 1 - www.micmac.ign.fr4/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  17. 17. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Outline 2 Method description and Evaluation Criteria Method Workflow : Step 1/Step 2 Method Description : Step 1 Evaluation criteria5/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  18. 18. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Method Workflow Input Data Database (DB) to update satellite images DSM Orthophotos6/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  19. 19. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Method Workflow Input Data Database (DB) to update satellite images nDSM DSM DTM Orthophotos6/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  20. 20. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Method Workflow Input Step 1 Data Matching Database (DB) to update Robust Primitives satellite images nDSM Decision DSM DTM Database partially Orthophotos updated Identifying non-changed buildings . . . . . . and changed buildings6/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  21. 21. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Method Workflow Input Step 1 Data Matching Database (DB) to update Robust Primitives satellite images nDSM Decision updated DSM DTM DTM Database partially Orthophotos updated nDSM Identifying non-changed buildings . . . . . . and changed buildings6/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  22. 22. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Method Workflow Input Step 1 Data Matching Database (DB) to update Robust Primitives satellite images nDSM Decision updated DSM DTM DTM Database partially Orthophotos updated nDSM Identifying non-changed buildings . . . . . . and changed buildings Overground Mask t Overground Mask t-1 Morphological comparison New Building Step 2 detection6/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  23. 23. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Method Workflow Input Step 1 Data Matching Database (DB) to update Robust Primitives satellite images nDSM Decision updated DSM DTM DTM Database partially Orthophotos updated nDSM Identifying non-changed buildings . . . . . . and changed buildings Overground Mask t Overground Mask t-1 Morphological comparison New Building Step 2 detection6/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  24. 24. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Definition Definition a Digital Terrain Model (DTM) = a digital representation of the ground surface DSM DTM7/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  25. 25. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Method used for processing the DTM 230m 200m DSM Method described in (Champion et al., 2009) N. Champion, D. Boldo, M. Pierrot-Deseilligny and G. Stamon. Automatic estimation of fine terrain models from multiple high resolution images. In : Proc. of the ICIP Conference, 2009.8/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  26. 26. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Method used for processing the DTM 230m 200m DSM Building and Vegetation Mask Method described in (Champion et al., 2009) N. Champion, D. Boldo, M. Pierrot-Deseilligny and G. Stamon. Automatic estimation of fine terrain models from multiple high resolution images. In : Proc. of the ICIP Conference, 2009.8/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  27. 27. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Method used for processing the DTM 230m 200m DSM Building and Vegetation Mask DTM Method described in (Champion et al., 2009) N. Champion, D. Boldo, M. Pierrot-Deseilligny and G. Stamon. Automatic estimation of fine terrain models from multiple high resolution images. In : Proc. of the ICIP Conference, 2009.8/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  28. 28. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) Matching Database (DB) to update Robust Primitives satellite images nDSM Decision updated DSM DTM DTM Database partially Orthophotos updated nDSM Identifying non-changed buildings . . . . . . and changed buildings Overground Mask t Overground Mask t-1 Morphological comparison New Building detection9/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  29. 29. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) Step 1 Matching Database (DB) to update Robust Primitives satellite images nDSM Decision updated DSM DTM DTM Database partially Orthophotos updated nDSM Identifying non-changed buildings . . . . . . and changed buildings Overground Mask t Overground Mask t-1 Morphological comparison New Building detection9/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  30. 30. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) Step 1 Matching Database (DB) to update Robust Primitives satellite images nDSM Decision updated DSM DTM DTM Database partially Orthophotos updated nDSM Identifying non-changed buildings . . . . . . and changed buildings Overground Mask t Overground Mask t-1 Morphological comparison New Building detection9/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  31. 31. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) Step 1 Matching Database (DB) to update Robust Primitives satellite images nDSM Decision updated DSM DTM DTM Database partially Orthophotos updated nDSM Identifying non-changed buildings . . . . . . and changed buildings Overground Mask t Overground Mask t-1 Morphological comparison New Building detection9/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  32. 32. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) Step 1 Matching Database (DB) to update Robust Primitives satellite images nDSM Decision updated DSM DTM DTM Database partially Orthophotos updated nDSM Identifying non-changed buildings . . . . . . and changed buildings Overground Mask t Overground Mask t-1 Morphological comparison New Building detection9/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  33. 33. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) : Robust Primitives (1) Information about Overground based on the nDSM = DSM – DTM 6m 0m input DSM nDSM10/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  34. 34. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) : Robust Primitives (1) Information about Overground based on the nDSM = DSM – DTM (2) Linear Primitives : 2D Contours extracted from the DSM (Deriche, 1987)10/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  35. 35. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) : Robust Primitives (1) Information about Overground based on the nDSM = DSM – DTM (2) Linear Primitives : 2D Contours extracted from the DSM (Deriche, 1987) (3) Linear Primitives : 3D Segments Reconstructed from input multiscopic images (Taillandier and Deriche, 2002)10/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  36. 36. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) Step 1 Matching Database (DB) to update Robust Primitives satellite images nDSM Decision updated DSM DTM DTM Database partially Orthophotos updated nDSM Identifying non-changed buildings . . . . . . and changed buildings Overground Mask t Overground Mask t-1 Morphological comparison New Building detection11/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  37. 37. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) Step 1 Matching Database (DB) to update Robust Primitives satellite images nDSM Decision updated DSM DTM DTM Database partially Orthophotos updated nDSM Identifying non-changed buildings . . . . . . and changed buildings Overground Mask t Overground Mask t-1 Morphological comparison New Building detection11/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  38. 38. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) : Matching and Decision based on the a contrario paradigm 1 Dissimilarity Scores nDSM Linear Primitives nDSM → SDissimilarity Linear Primitives (2D contours & 3D segments) → SDissimilarity 1. A. Desolneux, L. Moisan, and J.-M. Morel, Meaningful alignments. In : International Journal of Computer Vision, 1 :40, 2000.12/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  39. 39. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) : Matching and Decision based on the a contrario paradigm 1 Dissimilarity Scores nDSM Linear Primitives nDSM → SDissimilarity Linear Primitives (2D contours & 3D segments) → SDissimilarity nDSM Illustration of the computation of SDissimilarity 1. A. Desolneux, L. Moisan, and J.-M. Morel, Meaningful alignments. In : International Journal of Computer Vision, 1 :40, 2000.12/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  40. 40. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) : Matching and Decision based on the a contrario paradigm 1 Dissimilarity Scores nDSM Linear Primitives nDSM → SDissimilarity Linear Primitives (2D contours & 3D segments) → SDissimilarity nDSM Illustration of the computation of SDissimilarity nDSM12/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  41. 41. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) : Matching and Decision based on the a contrario paradigm 1 Dissimilarity Scores nDSM Linear Primitives nDSM → SDissimilarity Linear Primitives (2D contours & 3D segments) → SDissimilarity nDSM Illustration of the computation of SDissimilarity Building to verify nDSM12/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  42. 42. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) : Matching and Decision based on the a contrario paradigm 1 Dissimilarity Scores nDSM Linear Primitives nDSM → SDissimilarity Linear Primitives (2D contours & 3D segments) → SDissimilarity nDSM Illustration of the computation of SDissimilarity Area not covered by overground pixel in the nDSM Building to verify nDSM12/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  43. 43. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) : Matching and Decision based on the a contrario paradigm 1 Dissimilarity Scores nDSM Linear Primitives nDSM → SDissimilarity Linear Primitives (2D contours & 3D segments) → SDissimilarity nDSM Illustration of the computation of SDissimilarity Area not covered by overground pixel in the nDSM Another area with no coverage Building to verify nDSM12/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  44. 44. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) : Matching and Decision based on the a contrario paradigm 1 Dissimilarity Scores nDSM Linear Primitives nDSM → SDissimilarity Linear Primitives (2D contours & 3D segments) → SDissimilarity nDSM Illustration of the computation of SDissimilarity Area not covered by overground pixel in the nDSM Another area with no coverage Building to verify nDSM nDSM A (PBuilding (x,y) : nDSM(x,y) < TH ) SDissimilarity = ABuilding12/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  45. 45. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) : Matching and Decision based on the a contrario paradigm 1 Dissimilarity Scores nDSM Linear Primitives nDSM → SDissimilarity Linear Primitives (2D contours & 3D segments) → SDissimilarity Decision Rules Hypothesis : “a few changes in the scene” ⇒ “a building already present in the database is likely still to be in the scene at the time of investigation.” 1. A. Desolneux, L. Moisan, and J.-M. Morel, Meaningful alignments. In : International Journal of Computer Vision, 1 :40, 2000.12/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  46. 46. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) : Matching and Decision based on the a contrario paradigm 1 Dissimilarity Scores nDSM Linear Primitives nDSM → SDissimilarity Linear Primitives (2D contours & 3D segments) → SDissimilarity Decision Rules Hypothesis : “a few changes in the scene” ⇒ “a building already present in the database is likely still to be in the scene at the time of investigation.” ⇒ A weak indication is enough to validate it during Step 1 1. A. Desolneux, L. Moisan, and J.-M. Morel, Meaningful alignments. In : International Journal of Computer Vision, 1 :40, 2000.12/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  47. 47. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) : Matching and Decision based on the a contrario paradigm 1 Dissimilarity Scores nDSM Linear Primitives nDSM → SDissimilarity Linear Primitives (2D contours & 3D segments) → SDissimilarity Decision Rules Hypothesis : “a few changes in the scene” ⇒ “a building already present in the database is likely still to be in the scene at the time of investigation.” ⇒ A weak indication is enough to validate it during Step 1 Linear Primitives nDSM ⇒ A small value for SDissimilarity or SDissimilarity 1. A. Desolneux, L. Moisan, and J.-M. Morel, Meaningful alignments. In : International Journal of Computer Vision, 1 :40, 2000.12/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  48. 48. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) : Matching and Decision based on the a contrario paradigm 1 Dissimilarity Scores nDSM Linear Primitives nDSM → SDissimilarity Linear Primitives (2D contours & 3D segments) → SDissimilarity Decision Rules Hypothesis : “a few changes in the scene” ⇒ “a building already present in the database is likely still to be in the scene at the time of investigation.” ⇒ A weak indication is enough to validate it during Step 1 Linear Primitives nDSM ⇒ A small value for SDissimilarity or SDissimilarity {Unchanged Buildings} = Buildingi : SnDSM (Buildingi ) < Dissimilarity M1 i∈[1,N] ∪ Linear Primitives Buildingi : SDissimilarity (Buildingi ) < M2 i∈[1,N] 1. A. Desolneux, L. Moisan, and J.-M. Morel, Meaningful alignments. In : International Journal of Computer Vision, 1 :40, 2000.12/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  49. 49. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Database Verification (Step 1) : Matching and Decision based on the a contrario paradigm 1 Dissimilarity Scores nDSM Linear Primitives nDSM → SDissimilarity Linear Primitives (2D contours & 3D segments) → SDissimilarity Decision Rules Hypothesis : “a few changes in the scene” ⇒ “a building already present in the database is likely still to be in the scene at the time of investigation.” ⇒ A weak indication is enough to validate it during Step 1 Linear Primitives nDSM ⇒ A small value for SDissimilarity or SDissimilarity {Unchanged Buildings} = Buildingi : SnDSM (Buildingi ) < Dissimilarity M1 i∈[1,N] ∪ Linear Primitives Buildingi : SDissimilarity (Buildingi ) < M2 i∈[1,N] ⇒ Our change detection system is actually a no-change detection system : changes are inferred from {Unchanged Buildings} by taking the complementary 1. A. Desolneux, L. Moisan, and J.-M. Morel, Meaningful alignments. In : International Journal of Computer Vision, 1 :40, 2000.12/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  50. 50. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Method output - Illustration on Amiens Pont de Metz (GSD = 50cm) Change Map Evaluation Map unchanged modified demolished new TP FN FP TN13/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  51. 51. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Evaluation Criteria Success criteria used in this project (Champion et al., 2009) 1 Confusion Matrix XX XXX System (S) Unchanged (S) M&D2 (S) New (S) Bg.3 (S) Ref.1 (R) XX X X Unchanged (R) # NU(S)→U(R) # NC(S)→U(R) - - M&D2 (R) # NU(S)→C(R) # NC(S)→C(R) - - New (R) - - # NN(S)→N(R) # NB(S)→N(R) Bg.3 (R) - - # NN(S)→B(R) - 1 Ref. = Reference 2 M&D = Modified and Demolished 3 Bg. = Background 1. N. Champion, F. Rottensteiner, L. Matikainen, X. Liang, J. Hyyppa, and B. Olsen, “A test of automatic building change detection approaches.,” in International Archives of Photogrammetry and Remote Sensing, Vol. 38, Part 3/W4, 2009.14/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  52. 52. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Evaluation Criteria Success criteria used in this project (Champion et al., 2009) 1 Confusion Matrix XX XXX System (S) Unchanged (S) M&D2 (S) New (S) Bg.3 (S) Ref.1 (R) XXX X Unchanged (R) # NU(S)→U(R) # NC(S)→U(R) - - M&D2 (R) # NU(S)→C(R) # NC(S)→C(R) - - New (R) - - # NN(S)→N(R) # NB(S)→N(R) Bg.3 (R) - - # NN(S)→B(R) - Computing the Recall and Precision for each change class - e.g. : 1 Ref. = Reference 2 M&D = Modified and Demolished 3 Bg. = Background 1. N. Champion, F. Rottensteiner, L. Matikainen, X. Liang, J. Hyyppa, and B. Olsen, “A test of automatic building change detection approaches.,” in International Archives of Photogrammetry and Remote Sensing, Vol. 38, Part 3/W4, 2009.14/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  53. 53. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Evaluation Criteria Success criteria used in this project (Champion et al., 2009) 1 Confusion Matrix XXX XXX (S) System Unchanged (S) M&D2 (S) New (S) Bg.3 (S) Ref.1 (R) XX X Unchanged (R) # NU(S)→U(R) # NC(S)→U(R) - - M&D2 (R) # NU(S)→C(R) # NC(S)→C(R) - - New (R) - - # NN(S)→N(R) # NB(S)→N(R) Bg.3 (R) - - # NN(S)→B(R) - Computing the Recall and Precision for each change class - e.g. : #NC(S)→C(R) RecallM&D = #NC(S)→C(R) + #NU(S)→C(R) 1 Ref. = Reference 2 M&D = Modified and Demolished 3 Bg. = Background 1. N. Champion, F. Rottensteiner, L. Matikainen, X. Liang, J. Hyyppa, and B. Olsen, “A test of automatic building change detection approaches.,” in International Archives of Photogrammetry and Remote Sensing, Vol. 38, Part 3/W4, 2009.14/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  54. 54. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Evaluation Criteria Success criteria used in this project (Champion et al., 2009) 1 Confusion Matrix XXX XXX (S) System Unchanged (S) M&D2 (S) New (S) Bg.3 (S) Ref.1 (R) XX X Unchanged (R) # NU(S)→U(R) # NC(S)→U(R) - - M&D2 (R) # NU(S)→C(R) # NC(S)→C(R) - - New (R) - - # NN(S)→N(R) # NB(S)→N(R) Bg.3 (R) - - # NN(S)→B(R) - Computing the Recall and Precision for each change class - e.g. : #NC(S)→C(R) #NC(S)→C(R) RecallM&D = / PrecisionM&D = #NC(S)→C(R) + #NU(S)→C(R) #NC(S)→C(R) + #NC(S)→U(R) 1 Ref. = Reference 2 M&D = Modified and Demolished 3 Bg. = Background 1. N. Champion, F. Rottensteiner, L. Matikainen, X. Liang, J. Hyyppa, and B. Olsen, “A test of automatic building change detection approaches.,” in International Archives of Photogrammetry and Remote Sensing, Vol. 38, Part 3/W4, 2009.14/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  55. 55. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Evaluation Criteria Success criteria used in this project (Champion et al., 2009) 1 Confusion Matrix XX XXX System (S) Unchanged (S) M&D2 (S) New (S) Bg.3 (S) Ref.1 (R) XXX X Unchanged (R) # NU(S)→U(R) # NC(S)→U(R) - - M&D2 (R) # NU(S)→C(R) # NC(S)→C(R) - - New (R) - - # NN(S)→N(R) # NB(S)→N(R) Bg.3 (R) - - # NN(S)→B(R) - Computing the Recall and Precision for each change class 1st success criterion (qualitative effectiveness) ⇒ a high recall for all changes = Demolished & Modified and New 1 Ref. = Reference 2 M&D = Modified and Demolished 3 Bg. = Background 1. N. Champion, F. Rottensteiner, L. Matikainen, X. Liang, J. Hyyppa, and B. Olsen, “A test of automatic building change detection approaches.,” in International Archives of Photogrammetry and Remote Sensing, Vol. 38, Part 3/W4, 2009.14/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  56. 56. General and specific contexts Method Workflow : Step 1/Step 2 Method description and Evaluation Criteria Method Description : Step 1 Experiments Evaluation criteria Conclusion Evaluation Criteria Success criteria used in this project (Champion et al., 2009) 1 Confusion Matrix XXX XXX (S) System Unchanged (S) M&D2 (S) New (S) Bg.3 (S) Ref.1 (R) XX X Unchanged (R) # NU(S)→U(R) # NC(S)→U(R) - - M&D2 (R) # NU(S)→C(R) # NC(S)→C(R) - - New (R) - - # NN(S)→N(R) # NB(S)→N(R) Bg.3 (R) - - # NN(S)→B(R) - Computing the Recall and Precision for each change class 1st success criterion (qualitative effectiveness) ⇒ a high recall for all changes = Demolished & Modified and New 2nd success criterion (economical effectiveness) to limit the number of false alarms ⇒ a high precision for modified and demolished buildings ⇔ a high recall for unchanged buildings (a lower precision for new buildings is not critical) 1 Ref. = Reference 2 M&D = Modified and Demolished 3 Bg. = Background 1. N. Champion, F. Rottensteiner, L. Matikainen, X. Liang, J. Hyyppa, and B. Olsen, “A test of automatic building change detection approaches.,” in International Archives of Photogrammetry and Remote Sensing, Vol. 38, Part 3/W4, 2009.14/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  57. 57. General and specific contexts Method description and Evaluation Criteria Evaluation at 50cm Experiments Comparison 50cm/70cm Conclusion Evaluation at 50cm Analysis Quantitative Evaluation (50cm) Unchanged M&D1 New Amiens Downtown Recall 95,7 95,45 80,3 Precision 99,8 48,09 28,49 Amiens Pont-de-Metz Recall 84,98 96,55 92,13 Precision 98,57 69,65 38,86 Toulouse Rocade Recall 80,52 96,99 76,47 Precision 99,55 37,61 18,44 1 - Modified and Demolished15/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  58. 58. General and specific contexts Method description and Evaluation Criteria Evaluation at 50cm Experiments Comparison 50cm/70cm Conclusion Evaluation at 50cm Analysis Recall (Qualitative Effectiveness) Quantitative Evaluation (50cm) very good for modified and demolished buildings ( 95%) Unchanged M&D1 New Amiens Downtown Recall 95,7 95,45 80,3 Precision 99,8 48,09 28,49 Amiens Pont-de-Metz Recall 84,98 96,55 92,13 Precision 98,57 69,65 38,86 Toulouse Rocade Recall 80,52 96,99 76,47 Precision 99,55 37,61 18,44 1 - Modified and Demolished15/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  59. 59. General and specific contexts Method description and Evaluation Criteria Evaluation at 50cm Experiments Comparison 50cm/70cm Conclusion Evaluation at 50cm Analysis Recall (Qualitative Effectiveness) Quantitative Evaluation (50cm) very good for modified and demolished buildings ( 95%) Unchanged M&D1 New good for new buildings (on Amiens Downtown average 85%) . . . Recall 95,7 95,45 80,3 Precision 99,8 48,09 28,49 Amiens Pont-de-Metz Recall 84,98 96,55 92,13 Precision 98,57 69,65 38,86 Toulouse Rocade Recall 80,52 96,99 76,47 Precision 99,55 37,61 18,44 1 - Modified and Demolished15/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  60. 60. General and specific contexts Method description and Evaluation Criteria Evaluation at 50cm Experiments Comparison 50cm/70cm Conclusion Evaluation at 50cm Analysis Recall (Qualitative Effectiveness) Quantitative Evaluation (50cm) very good for modified and demolished buildings ( 95%) Unchanged M&D1 New good for new buildings (on Amiens Downtown average 85%) . . . but sometimes Recall 95,7 95,45 80,3 critical (< 80%) Precision 99,8 48,09 28,49 Amiens Pont-de-Metz Recall 84,98 96,55 92,13 Precision 98,57 69,65 38,86 Toulouse Rocade Recall 80,52 96,99 76,47 Precision 99,55 37,61 18,44 1 - Modified and Demolished15/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  61. 61. General and specific contexts Method description and Evaluation Criteria Evaluation at 50cm Experiments Comparison 50cm/70cm Conclusion Evaluation at 50cm Analysis Recall (Qualitative Effectiveness) Quantitative Evaluation (50cm) very good for modified and demolished buildings ( 95%) Unchanged M&D1 New good for new buildings (on Amiens Downtown average 85%) . . . but sometimes Recall 95,7 95,45 80,3 critical (< 80%) Precision 99,8 48,09 28,49 Precision (Economical Effectiveness) Amiens Pont-de-Metz relatively low for new buildings Recall 84,98 96,55 92,13 Precision 98,57 69,65 38,86 Toulouse Rocade Recall 80,52 96,99 76,47 Precision 99,55 37,61 18,44 1 - Modified and Demolished15/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  62. 62. General and specific contexts Method description and Evaluation Criteria Evaluation at 50cm Experiments Comparison 50cm/70cm Conclusion Evaluation at 50cm Analysis Recall (Qualitative Effectiveness) Quantitative Evaluation (50cm) very good for modified and demolished buildings ( 95%) Unchanged M&D1 New good for new buildings (on Amiens Downtown average 85%) . . . but sometimes Recall 95,7 95,45 80,3 critical (< 80%) Precision 99,8 48,09 28,49 Precision (Economical Effectiveness) Amiens Pont-de-Metz relatively low for new buildings Recall 84,98 96,55 92,13 → < 20% in Toulouse Rocade Precision 98,57 69,65 38,86 Toulouse Rocade Recall 80,52 96,99 76,47 Precision 99,55 37,61 18,44 1 - Modified and Demolished15/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery
  63. 63. General and specific contexts Method description and Evaluation Criteria Evaluation at 50cm Experiments Comparison 50cm/70cm Conclusion Evaluation at 50cm Analysis Recall (Qualitative Effectiveness) Quantitative Evaluation (50cm) very good for modified and demolished buildings ( 95%) Unchanged M&D1 New good for new buildings (on Amiens Downtown average 85%) . . . but sometimes Recall 95,7 95,45 80,3 critical (< 80%) Precision 99,8 48,09 28,49 Precision (Economical Effectiveness) Amiens Pont-de-Metz relatively low for new buildings Recall 84,98 96,55 92,13 → < 20% in Toulouse Rocade Precision 98,57 69,65 38,86 better for demolished and Toulouse Rocade modified buildings (70% in Recall 80,52 96,99 76,47 Amiens Pont-de-Metz) Precision 99,55 37,61 18,44 1 - Modified and Demolished15/18 Champion et al. IGARSS 2011 2D Change Detection from satellite imagery

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