Implementing kohonen's som with missing data in OTB

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Implementing kohonen's som with missing data in OTB
Gregoire Mercier; Institut Telecom; Telecom Bretagne
Bassam Abdel Latif; Institut Telecom; Telecom Bretagne

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Implementing kohonen's som with missing data in OTB

  1. 1. Implementing Kohonen’s SOM with Missing Data in OTB Gr´goire Mercier and Bassam Abdel Latif e Institut Telecom; Telecom Bretagne CNRS UMR 3192 lab-STICC, team CID Technopole Brest-Iroise, CS 83818, F-29238 Brest Cedex 3, France IGARSS, 2009
  2. 2. The Kohonen Map Properties Clouds and Shadows in times series Description Benefits in Generic Programming Contents 1 The Kohonen Map Properties Description 2 Clouds and Shadows in times series Modification to deal with missing value Modification to deal with erroneous value 3 Benefits in Generic Programming page 2 G Mercier & B Abdel Latif Kohonen map in the OTB
  3. 3. The Kohonen Map Properties Clouds and Shadows in times series Description Benefits in Generic Programming The Kohonen Map Properties Neural network of 1 fully connected layer to be trained by supervized or unsupervized approaches Corresponds to a transformation R n =⇒ R, R 2 or R 3 • Visualization • Compression Neurons in their neighborhood give similar response Self-Organizing Map (SOM) • Clustering Often use when neighborhood of a class has significant meaning • Classification • Pattern recognition • Data mining • ... page 3 G Mercier & B Abdel Latif Kohonen map in the OTB
  4. 4. The Kohonen Map Properties Clouds and Shadows in times series Description Benefits in Generic Programming The Kohonen Map Properties Neural network of 1 fully connected layer to be trained by supervized or unsupervized approaches Corresponds to a transformation R n =⇒ R, R 2 or R 3 • Visualization • Compression Neurons in their neighborhood give similar response Self-Organizing Map (SOM) • Clustering Often use when neighborhood of a class has significant meaning • Classification • Pattern recognition • Data mining • ... page 3 G Mercier & B Abdel Latif Kohonen map in the OTB
  5. 5. The Kohonen Map Properties Clouds and Shadows in times series Description Benefits in Generic Programming The Kohonen Map Properties Neural network of 1 fully connected layer to be trained by supervized or unsupervized approaches Corresponds to a transformation R n =⇒ R, R 2 or R 3 • Visualization • Compression Neurons in their neighborhood give similar response Self-Organizing Map (SOM) • Clustering Often use when neighborhood of a class has significant meaning • Classification • Pattern recognition • Data mining • ... page 3 G Mercier & B Abdel Latif Kohonen map in the OTB
  6. 6. The Kohonen Map Properties Clouds and Shadows in times series Description Benefits in Generic Programming The Kohonen Map Properties Neural network of 1 fully connected layer to be trained by supervized or unsupervized approaches Corresponds to a transformation R n =⇒ R, R 2 or R 3 • Visualization • Compression Neurons in their neighborhood give similar response Self-Organizing Map (SOM) • Clustering Often use when neighborhood of a class has significant meaning • Classification • Pattern recognition • Data mining • ... page 3 G Mercier & B Abdel Latif Kohonen map in the OTB
  7. 7. The Kohonen Map Properties Clouds and Shadows in times series Description Benefits in Generic Programming Training Illustration Neurons random initialization (cm ) Select randomly a training sample x and find the winning neuron cmx : x − cmx = min x − cm m∈{1,...,M} Update the weight of the winning neuron and of its neighborhood: cm (t + 1) = cm (t) + hm,mx (t)[x(t) − cm (t)] Loop until convergence page 4 G Mercier & B Abdel Latif Kohonen map in the OTB
  8. 8. The Kohonen Map Properties Clouds and Shadows in times series Description Benefits in Generic Programming Training Illustration Neurons random initialization (cm ) Select randomly a training sample x and find the winning neuron cmx : x − cmx = min x − cm m∈{1,...,M} Update the weight of the winning neuron and of its neighborhood: cm (t + 1) = cm (t) + hm,mx (t)[x(t) − cm (t)] Loop until convergence page 4 G Mercier & B Abdel Latif Kohonen map in the OTB
  9. 9. The Kohonen Map Properties Clouds and Shadows in times series Description Benefits in Generic Programming Training Illustration Neurons random initialization (cm ) Select randomly a training sample x and find the winning neuron cmx : x − cmx = min x − cm m∈{1,...,M} Update the weight of the winning neuron and of its neighborhood: cm (t + 1) = cm (t) + hm,mx (t)[x(t) − cm (t)] Loop until convergence page 4 G Mercier & B Abdel Latif Kohonen map in the OTB
  10. 10. The Kohonen Map Properties Clouds and Shadows in times series Description Benefits in Generic Programming Training Illustration Neurons random initialization (cm ) Select randomly a training sample x and find the winning neuron cmx : x − cmx = min x − cm m∈{1,...,M} Update the weight of the winning neuron and of its neighborhood: cm (t + 1) = cm (t) + hm,mx (t)[x(t) − cm (t)] Loop until convergence page 4 G Mercier & B Abdel Latif Kohonen map in the OTB
  11. 11. The Kohonen Map Properties Clouds and Shadows in times series Description Benefits in Generic Programming Training Illustration Neurons random initialization (cm ) Select randomly a training sample x and find the winning neuron cmx : x − cmx = min x − cm m∈{1,...,M} Update the weight of the winning neuron and of its neighborhood: cm (t + 1) = cm (t) + hm,mx (t)[x(t) − cm (t)] Loop until convergence page 4 G Mercier & B Abdel Latif Kohonen map in the OTB
  12. 12. The Kohonen Map Properties Clouds and Shadows in times series Description Benefits in Generic Programming Training Illustration Neurons random initialization (cm ) Select randomly a training sample x and find the winning neuron cmx : x − cmx = min x − cm m∈{1,...,M} Update the weight of the winning neuron and of its neighborhood: cm (t + 1) = cm (t) + hm,mx (t)[x(t) − cm (t)] Loop until convergence page 4 G Mercier & B Abdel Latif Kohonen map in the OTB
  13. 13. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Contents 1 The Kohonen Map Properties Description 2 Clouds and Shadows in times series Modification to deal with missing value Modification to deal with erroneous value 3 Benefits in Generic Programming page 5 G Mercier & B Abdel Latif Kohonen map in the OTB
  14. 14. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Problem: 10 dates in a MODIS time series over Brittany, France 25-nov-2002 5-jan-2003 24-jan-2003 4-feb-2003 22-feb-2003 15-mar-2003 17-mar-2003 19-mar-2003 7-apr-2003 16-apr-2003 MODIS time series (NIR band, 250m resol. 10 dates). Images choosen with a cloud coverage below 50%, zenithal angle below 20◦ overt center Erroneous data, of Brittany. clouds page 6 G Mercier & B Abdel Latif Kohonen map in the OTB
  15. 15. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Problem: 10 dates in a MODIS time series over Brittany, France 25-nov-2002 5-jan-2003 24-jan-2003 4-feb-2003 22-feb-2003 15-mar-2003 17-mar-2003 19-mar-2003 7-apr-2003 16-apr-2003 MODIS time series (NIR band, 250m resol. 10 dates). Images choosen with a cloud coverage below 50%, zenithal angle below 20◦ overt center Erroneous Data, of Brittany. clouds page 6 G Mercier & B Abdel Latif Kohonen map in the OTB
  16. 16. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Problem: 10 dates in a MODIS time series over Brittany, France 25-nov-2002 5-jan-2003 24-jan-2003 4-feb-2003 22-feb-2003 15-mar-2003 17-mar-2003 19-mar-2003 7-apr-2003 16-apr-2003 MODIS time series (NIR band, 250m resol. 10 dates). Images choosen with a cloud coverage below 50%, zenithal angle below 20◦ overt center Missing Data, sensor of Brittany. page 6 G Mercier & B Abdel Latif Kohonen map in the OTB
  17. 17. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Problem: 10 dates in a MODIS time series over Brittany, France 25-nov-2002 5-jan-2003 24-jan-2003 4-feb-2003 22-feb-2003 15-mar-2003 17-mar-2003 19-mar-2003 7-apr-2003 16-apr-2003 MODIS time series (NIR band, 250m resol. 10 dates). Images choosen with a cloud coverage below 50%, zenithal angle below 20◦ overt center Erroneous Data, of Brittany. clouds page 6 G Mercier & B Abdel Latif Kohonen map in the OTB
  18. 18. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Problem: 10 dates in a MODIS time series over Brittany, France 25-nov-2002 5-jan-2003 24-jan-2003 4-feb-2003 22-feb-2003 15-mar-2003 17-mar-2003 19-mar-2003 7-apr-2003 16-apr-2003 MODIS time series (NIR band, 250m resol. 10 dates). Images choosen with a cloud coverage below 50%, zenithal angle below 20◦ overt center Erroneous Data, of Brittany. clouds page 6 G Mercier & B Abdel Latif Kohonen map in the OTB
  19. 19. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Problem: 10 dates in a MODIS time series over Brittany, France 25-nov-2002 5-jan-2003 24-jan-2003 4-feb-2003 22-feb-2003 15-mar-2003 17-mar-2003 19-mar-2003 7-apr-2003 16-apr-2003 MODIS time series (NIR band, 250m resol. 10 dates). Images choosen with a cloud coverage below 50%, zenithal angle below 20◦ overt center Clean Data of Brittany. page 6 G Mercier & B Abdel Latif Kohonen map in the OTB
  20. 20. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Problem: 10 dates in a MODIS time series over Brittany, France 25-nov-2002 5-jan-2003 24-jan-2003 4-feb-2003 22-feb-2003 15-mar-2003 17-mar-2003 19-mar-2003 7-apr-2003 16-apr-2003 MODIS time series (NIR band, 250m resol. 10 dates). Images choosen with a cloud coverage below 50%, zenithal angle below 20◦ overt center Missing Data, sensor of Brittany. page 6 G Mercier & B Abdel Latif Kohonen map in the OTB
  21. 21. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Problem: 10 dates in a MODIS time series over Brittany, France 25-nov-2002 5-jan-2003 24-jan-2003 4-feb-2003 22-feb-2003 15-mar-2003 17-mar-2003 19-mar-2003 7-apr-2003 16-apr-2003 MODIS time series (NIR band, 250m resol. 10 dates). Images choosen with a cloud coverage below 50%, zenithal angle below 20◦ overt center Erroneous Data, of Brittany. clouds page 6 G Mercier & B Abdel Latif Kohonen map in the OTB
  22. 22. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Problem: 10 dates in a MODIS time series over Brittany, France 25-nov-2002 5-jan-2003 24-jan-2003 4-feb-2003 22-feb-2003 15-mar-2003 17-mar-2003 19-mar-2003 7-apr-2003 16-apr-2003 MODIS time series (NIR band, 250m resol. 10 dates). Images choosen with a cloud coverage below 50%, zenithal angle below 20◦ overt center Clean Data of Brittany. page 6 G Mercier & B Abdel Latif Kohonen map in the OTB
  23. 23. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Problem: 10 dates in a MODIS time series over Brittany, France 25-nov-2002 5-jan-2003 24-jan-2003 4-feb-2003 22-feb-2003 15-mar-2003 17-mar-2003 19-mar-2003 7-apr-2003 16-apr-2003 MODIS time series (NIR band, 250m resol. 10 dates). Images choosen with a cloud coverage below 50%, zenithal angle below 20◦ overt center Erroneous Data, of Brittany. sensor page 6 G Mercier & B Abdel Latif Kohonen map in the OTB
  24. 24. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming From Erroneous to Missing data Type of errors Presence of clouds (± thickness) Presence of shadow Impulse noise outliers Simple Cloud/Shadow Detector Based on the Box and Whisker technique, rank statistics let x = {x1 , . . . , xk , . . .} be a time series ˛ ` ´˛ xk is erroneous if ˛xk − α x3/4 − x1/4 ˛ > |x1/2 |, α = 1.5 Neuron definition x = {NIR1 , . . . , NIRk , . . . , NIR10 } where NIRi = stands for NIR band of MODIS at time i page 7 G Mercier & B Abdel Latif Kohonen map in the OTB
  25. 25. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming From Erroneous to Missing data Type of errors Presence of clouds (± thickness) Presence of shadow Impulse noise outliers Simple Cloud/Shadow Detector Based on the Box and Whisker technique, rank statistics let x = {x1 , . . . , xk , . . .} be a time series ˛ ` ´˛ xk is erroneous if ˛xk − α x3/4 − x1/4 ˛ > |x1/2 |, α = 1.5 Neuron definition x = {NIR1 , . . . , NIRk , . . . , NIR10 } where NIRi = stands for NIR band of MODIS at time i page 7 G Mercier & B Abdel Latif Kohonen map in the OTB
  26. 26. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming From Erroneous to Missing data Type of errors Presence of clouds (± thickness) Presence of shadow Impulse noise outliers Simple Cloud/Shadow Detector Based on the Box and Whisker technique, rank statistics let x = {x1 , . . . , xk , . . .} be a time series ˛ ` ´˛ xk is erroneous if ˛xk − α x3/4 − x1/4 ˛ > |x1/2 |, α = 1.5 Neuron definition x = {NIR1 , . . . , NIRk , . . . , NIR10 } where NIRi = stands for NIR band of MODIS at time i page 7 G Mercier & B Abdel Latif Kohonen map in the OTB
  27. 27. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Kohonen Map Modification Deal with missing value In a series x = {x1 , . . . , xk , . . .} if xk is missing ⇐⇒ k ∈ Mx Where Mx is a set that contains indices of missing components Winning Neuron Evaluation X “ ”2 2 x − cm = x(j) − cm (j) j ∈Mx / Winning Neuron Update cmx ( h i cm;k (t) + hm,mx (t) xk − cm;k (t) if k ∈ Mx , / cm;k (t + 1) = cm;k (t) if k ∈ Mx . page 8 G Mercier & B Abdel Latif Kohonen map in the OTB
  28. 28. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Results Initial NIR Band Associated neuron value 25-nov-2002 25-nov-2002 page 9 G Mercier & B Abdel Latif Kohonen map in the OTB
  29. 29. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Results Initial NIR Band Associated neuron value 5-jan-2003 5-jan-2003 page 9 G Mercier & B Abdel Latif Kohonen map in the OTB
  30. 30. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Results Initial NIR Band Associated neuron value 24-jan-2003 24-jan-2003 page 9 G Mercier & B Abdel Latif Kohonen map in the OTB
  31. 31. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Results Initial NIR Band Associated neuron value 4-feb-2003 4-feb-2003 page 9 G Mercier & B Abdel Latif Kohonen map in the OTB
  32. 32. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Results Initial NIR Band Associated neuron value 19-mar-2003 19-mar-2003 page 9 G Mercier & B Abdel Latif Kohonen map in the OTB
  33. 33. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Results Initial NIR Band Associated neuron value 16-apr-2003 16-apr-2003 page 9 G Mercier & B Abdel Latif Kohonen map in the OTB
  34. 34. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Validation Comparison with compositing products of MODIS/Terra Kohonen MOD13Q1 MOD09Q1 CV-MVC through 16 days CV-MVC through 8 days 18-jan-2003 ` 02-feb-2003 a 18-jan-2003 ` 25-jan-2003 a page 10 G Mercier & B Abdel Latif Kohonen map in the OTB
  35. 35. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Appropriated Similarity Measure Vanishing outiers impact Euclidean Measure (no detection): n X ED(x, y) = (xi − yi )2 i=1 page 11 G Mercier & B Abdel Latif Kohonen map in the OTB
  36. 36. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Appropriated Similarity Measure Vanishing outiers impact Euclidean Measure (no detection): n X ED(x, y) = (xi − yi )2 i=1 Euclidean Measure (with missing value): X ED(x, y) = (xi − yi )2 i ∈Mx / page 11 G Mercier & B Abdel Latif Kohonen map in the OTB
  37. 37. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Appropriated Similarity Measure Vanishing outiers impact Euclidean Measure (no detection): n X ED(x, y) = (xi − yi )2 i=1 Euclidean Measure (with missing value): X ED(x, y) = (xi − yi )2 i ∈Mx / Sparse Measure: n X D(x, y) = |xia − yia |b , b > 0, a>0 i=1 page 11 G Mercier & B Abdel Latif Kohonen map in the OTB
  38. 38. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming How to set parameters a and b a, b = 1 100 b, a = 1 100 a =0.1 Correct matching % 80 Correct Matching % 80 a=1 60 60 40 40 20 20 0 0 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0 0.5 1 1.5 2 2.5 3 a or b b 100 Same distribution 0 < a, b < 1. Correct Matching % 80 fit ≈ 100% when b = 0.1. 60 He D(x, y) = i |xi − yi |0.1 avy P -ta ile 40 d 20 0 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 b page 12 G Mercier & B Abdel Latif Kohonen map in the OTB
  39. 39. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Comparison with other distances pPn a) Euclidean Distance ED(x, y) = i=1 (xi − yi )2 „ « −1 <x, y> b) Spectral Angle SA(x, y) = cos x y E(xy−¯¯ xy c) Spectral Correlation SCorr(x, y) = σx σy d) Spectral Info Divergence SID(x, y) = D(x y) + D(y x) |xi − yi |0.1 P e) Sparse Distance D(x, y) = i page 13 G Mercier & B Abdel Latif Kohonen map in the OTB
  40. 40. The Kohonen Map Modification to deal with missing value Clouds and Shadows in times series Modification to deal with erroneous value Benefits in Generic Programming Comparison with other distances Statistiques des images de diff´rence e a) ED b) SA c) SCorr d) SID e) Sparse D SOM (no outlier detection at training) µ -.03575 0.03665 0.03606 0.03878 -0.0002 σ .05594 0.13173 0.14953 0.1218 0.01607 SOM (with outlier detection at training) µ -0.00736 -0.00233 0.00228 -0.00246 -0.00018 σ 0.03516 0.08345 0.09807 0.07718 0.01609 page 13 G Mercier & B Abdel Latif Kohonen map in the OTB
  41. 41. The Kohonen Map Clouds and Shadows in times series Benefits in Generic Programming Contents 1 The Kohonen Map Properties Description 2 Clouds and Shadows in times series Modification to deal with missing value Modification to deal with erroneous value 3 Benefits in Generic Programming page 14 G Mercier & B Abdel Latif Kohonen map in the OTB
  42. 42. The Kohonen Map Clouds and Shadows in times series Benefits in Generic Programming Generic Programming The OTB code with Kohonen map Periodic SOM for training typedef itk::Statistics::EuclideanDistance< VectorType > DistanceType; typedef otb::SOMMap< VectorType, DistanceType, Dimension > MapType; typedef otb::PeriodicSOM< SampleListType, MapType, LearningBehaviorFunctorType, NeighborhoodBehaviorFunctorType > SOMType; SOMType::Pointer som = SOMType::New(); som->SetListSample( sampleList ); som->Set... som->Update(); page 15 G Mercier & B Abdel Latif Kohonen map in the OTB
  43. 43. The Kohonen Map Clouds and Shadows in times series Benefits in Generic Programming Generic Programming The OTB code with Kohonen map Periodic SOM for training SOM for classification typedef itk::Statistics::EuclideanDistance< VectorType > DistanceType; typedef otb::SOMMap< VectorType, DistanceType, Dimension > MapType; typedef otb::SOMClassifier<SampleType,SOMMapType,LabelPixelType> ClassifierType; ClassifierType::Pointer classifier = ClassifierType::New(); classifier->SetSample(sample.GetPointer()); classifier->SetMap(somreader->GetOutput()); classifier->Update(); page 15 G Mercier & B Abdel Latif Kohonen map in the OTB
  44. 44. The Kohonen Map Clouds and Shadows in times series Benefits in Generic Programming Generic Programming The OTB code with Kohonen map Periodic SOM for training SOM for classification SOM for segmentation typedef itk::Statistics::EuclideanDistance< VectorType > DistanceType; typedef otb::SOMMap< VectorType, DistanceType, Dimension > MapType; typedef otb::SOMbasedImageFilter<SampleType,SOMMapType,LabelPixelType> FilterType; FilterType::Pointer filter = FilterType::New(); filter->SetInputImage( inputImage ); filter->SetMap(somReader->GetOutput()); filter->Update(); page 15 G Mercier & B Abdel Latif Kohonen map in the OTB
  45. 45. The Kohonen Map Clouds and Shadows in times series Benefits in Generic Programming Generic Programming The OTB code with Kohonen map for missing value Periodic SOM for training with erroneous data typedef itk::Statistics::EuclideanDistance<VectorType> DistanceType; typedef otb::SOMMap< VectorType, DistanceType, Dimension > MapType; typedef otb::PeriodicSOM< SampleListType, MapType, LearningBehaviorFunctorType, NeighborhoodBehaviorFunctorType > SOMType; SOMType::Pointer som = SOMType::New(); som->SetListSample( sampleList ); som->Set... som->Update(); page 16 G Mercier & B Abdel Latif Kohonen map in the OTB
  46. 46. The Kohonen Map Clouds and Shadows in times series Benefits in Generic Programming Generic Programming The OTB code with Kohonen map for missing value Periodic SOM for training with erroneous data typedef otb::Statistics::EuclideanDistanceWithMissingValue<VectorType> DistanceType; typedef otb::SOMMap< VectorType, DistanceType, Dimension > MapType; typedef otb::PeriodicSOM< SampleListType, MapType, LearningBehaviorFunctorType, NeighborhoodBehaviorFunctorType > SOMType; SOMType::Pointer som = SOMType::New(); som->SetListSample( sampleList ); som->Set... som->Update(); page 16 G Mercier & B Abdel Latif Kohonen map in the OTB
  47. 47. The Kohonen Map Clouds and Shadows in times series Benefits in Generic Programming Generic Programming The OTB code with Kohonen map for missing value Periodic SOM for training with erroneous data typedef otb::Statistics::EuclideanDistanceWithMissingValue<VectorType> DistanceType; typedef otb::SOMMap< VectorType, DistanceType, Dimension > MapType; typedef otb::PeriodicSOM< SampleListType, MapType, LearningBehaviorFunctorType, NeighborhoodBehaviorFunctorType > SOMType; typedef otb::BoxAndWhiskerImageFilter<ImageType> CloudFilterType; SOMType::Pointer som = SOMType::New(); som->SetListSample( sampleList ); som->Set... som->Update(); page 16 G Mercier & B Abdel Latif Kohonen map in the OTB
  48. 48. The Kohonen Map Clouds and Shadows in times series Benefits in Generic Programming Generic Programming The OTB code with Kohonen map for missing value Periodic SOM for training with erroneous data SOM for recovering missing data typedef otb::Statistics::EuclideanDistanceWithMissingValue<VectorType> DistanceType; typedef otb::SOMMap< VectorType, DistanceType, Dimension > MapType; typedef otb::SOMbasedImageFilter<SampleType,SOMMapType,LabelPixelType> FilterType; FilterType::Pointer filter = FilterType::New(); filter->SetInputImage( inputImage ); filter->SetMap(somreader->GetOutput()); filter->Update(); page 16 G Mercier & B Abdel Latif Kohonen map in the OTB
  49. 49. The Kohonen Map Clouds and Shadows in times series Benefits in Generic Programming Generic Programming The OTB code with Kohonen map for missing value Periodic SOM for training with erroneous data SOM for recovering missing data SOM for recovering erroneous data typedef otb::Statistics::FlexibleDistanceWithMissingValue<VectorType> DistanceType; DistanceType::SetAlphaBeta(a,b); typedef otb::SOMMap< VectorType, DistanceType, Dimension > MapType; typedef otb::SOMbasedImageFilter<SampleType,SOMMapType,LabelPixelType> FilterType; FilterType::Pointer filter = FilterType::New(); filter->SetInputImage( inputImage ); filter->SetMap(somreader->GetOutput()); filter->Update(); page 16 G Mercier & B Abdel Latif Kohonen map in the OTB
  50. 50. The Kohonen Map Clouds and Shadows in times series Benefits in Generic Programming Generic Programming The OTB code with Kohonen map for missing value Periodic SOM for training with erroneous data SOM for recovering missing data SOM for recovering erroneous data SOM for what particular application? Set your own distance Set the neurone type Set the kohonen map training procedure Set the Kohonen map way of using... page 16 G Mercier & B Abdel Latif Kohonen map in the OTB
  51. 51. The Kohonen Map Clouds and Shadows in times series Benefits in Generic Programming Implementing Kohonen’s SOM with Missing Data in OTB Conclusion Generic tool for using missing and erroneous data Thematic part validated by the COSTEL for land use purpose May be adapted for many kind of supervized/unsupervised learning procedure based on the Kohonen map. page 17 G Mercier & B Abdel Latif Kohonen map in the OTB
  52. 52. The Kohonen Map Clouds and Shadows in times series Benefits in Generic Programming Implementing Kohonen’s SOM with Missing Data in OTB Conclusion Generic tool for using missing and erroneous data Thematic part validated by the COSTEL for land use purpose May be adapted for many kind of supervized/unsupervised learning procedure based on the Kohonen map. Just use it! page 17 G Mercier & B Abdel Latif Kohonen map in the OTB

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