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Basic Landcover Classification by LiDAR and Optical Data eCognition User Summit 2009 Munich November 2009
Contents ,[object Object],[object Object],[object Object],[object Object],[object Object]
Why basic landcover classification for the public sector? ,[object Object],[object Object],[object Object]
Which Data for Classification ,[object Object],[object Object],[object Object],DOP DSM DTM
Aim ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data processing ,[object Object],[object Object],[object Object],[object Object]
Object Primitives by Image Object Fusion ,[object Object],[object Object],[object Object],[object Object],Seed Candidate Candidate
Creation of Object  Primitives ,[object Object],[object Object],[object Object]
Classification Mean High  as a definite threshold NDVI  as fuzzy function Class A Class B Normalized Differenced Vegetation Index Mean Height Buidlings Normalized Differenced Vegetation Index (NDVI) Mean Height Vegetation Property Class
Improvement of Buildings
Appraisal of results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Elevated Objects Buildings Vegetation
Misclassification - Example ,[object Object],[object Object],Elevated Objects Buildings Vegetation
Misclassification - Example ,[object Object],[object Object],[object Object],[object Object]
Misclassification - Example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Additional Mask Layer RGBi Image Layer NDVI Layer non Vegetation Mask Elevated Objects Mask Layer arithmetic‘s ([Mean nir]-[Mean red])/([Mean nir]+[Mean red]) Layer  arithmetic’s 1: (NDVI ≥ 0); 0: (NDVI < 0) Layer arithmetic's  1: (NDVI ≥ 0) and (nDS > 1); 0: (NDVI < 0)
Classification improvement
Results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Buildings Vegetation [Abbildung]
Results
Building Generalization
Results – Building Generalization ,[object Object],[object Object]
Performance Tests ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Lessons learned ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Contact: Michael Pregesbauer State Government of Lower Austria Landhausplatz 1, A-3109 St.Poelten Tel.: ++43(0)2742/9005/13404 Mail.: michael.pregesbauer@noel.gv.at Thanks  to Christian Weise [Definiens AG] Gregor Willhauck [Definiens AG]

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E Cognition User Summit2009 Pregesbauer Geo Info Li Dar Basic Landcover

  • 1. Basic Landcover Classification by LiDAR and Optical Data eCognition User Summit 2009 Munich November 2009
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9. Classification Mean High as a definite threshold NDVI as fuzzy function Class A Class B Normalized Differenced Vegetation Index Mean Height Buidlings Normalized Differenced Vegetation Index (NDVI) Mean Height Vegetation Property Class
  • 11.
  • 12.
  • 13.
  • 14.
  • 15. Additional Mask Layer RGBi Image Layer NDVI Layer non Vegetation Mask Elevated Objects Mask Layer arithmetic‘s ([Mean nir]-[Mean red])/([Mean nir]+[Mean red]) Layer arithmetic’s 1: (NDVI ≥ 0); 0: (NDVI < 0) Layer arithmetic's 1: (NDVI ≥ 0) and (nDS > 1); 0: (NDVI < 0)
  • 17.
  • 20.
  • 21.
  • 22.
  • 23. Contact: Michael Pregesbauer State Government of Lower Austria Landhausplatz 1, A-3109 St.Poelten Tel.: ++43(0)2742/9005/13404 Mail.: michael.pregesbauer@noel.gv.at Thanks to Christian Weise [Definiens AG] Gregor Willhauck [Definiens AG]

Editor's Notes

  1. state government provide the territorial communities data (lu lc) among other – unter anderem balance - Bilanz