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E Cognition User Summit2009 Pregesbauer Geo Info Li Dar Basic Landcover
 

E Cognition User Summit2009 Pregesbauer Geo Info Li Dar Basic Landcover

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Basic Landcover Classification by LiDAR and Optical Data

Basic Landcover Classification by LiDAR and Optical Data

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E Cognition User Summit2009 Pregesbauer Geo Info Li Dar Basic Landcover E Cognition User Summit2009 Pregesbauer Geo Info Li Dar Basic Landcover Presentation Transcript

  • Basic Landcover Classification by LiDAR and Optical Data eCognition User Summit 2009 Munich November 2009
  • Contents
    • Overview – data resources
    • Object Generation
    • Class Definition and Classification
    • Classification results
    • remarks on data accuracy and data precision
  • Why basic landcover classification for the public sector?
    • legislative duties (e.g. land use planning, building regulation, sound wave propagation models)
    • planning purposes (e.g. infrastructure networks, soil sealing)
    • change detection (development of settlements)
  • Which Data for Classification
    • Digital Orthofotos , 4 Channels (Red, Green, Blue, near Infrared), 12.5cm Ground Sampling Distance
    • Digital Terrain Model , 1m Grid width
    • Digital Surface Model , 1m Grid width
    DOP DSM DTM
  • Aim
    • Classification of
    • Sealed areas
      • Buildings
      • Road Network
    • Vegetation (Forest Areas)
    • comprehensive data set for the whole state [20.000 km²]
    • stable classified classes (reliability ~ 95%)
  • Data processing
    • Dataset Tiling (2000x2000 Pixel)
    • Creation of Initial Objects
    • Stitching to Object Primitives
    • Classification
  • Object Primitives by Image Object Fusion
    • Object fusion based on a condition:
      • spectral difference
      • height difference
      • border condition
    Seed Candidate Candidate
  • Creation of Object Primitives
    • The class filter allows restricting the potential candidates by their classification.
    • Fitting function threshold allows to select a feature and a condition you want to optimize the fusion.
    • Depending on the fitting mode , one or more candidates will be merged with the seed image 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
    • Buildings
    • ~ 88 % accurately classified
    • ~ 9 % classified as elevated objects
    • ~ 3 % not classified
    • Vegetation
    • ~ 91 % accurately classified
    • ~ 5 % false classified
    • ~ 4% not classified
    Elevated Objects Buildings Vegetation
  • Misclassification - Example
    • Building Objects within Vegetation Areas
    • Elevated Objects next to Buildings
    Elevated Objects Buildings Vegetation
  • Misclassification - Example
    • Shadows
    • dark roofs
    • edges of buildings
    • lead to misclassifications at borders of buildings
  • Misclassification - Example
    • Object Properties
    • Mean NDVI > 0
    • Means nDS 4.7 m
    • Shadow Index > 0.07
    • Object Class: Building
    • enhanced approach : usage of masks
  • 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
    • Buildings
    • ~ 94.3 % accurately classified
    • ~ 5.2 % classified as elevated objects
    • ~ 0.5 % not classified
    • Vegetation
    • ~ 96.1% accurately classified
    • ~ 1.1 % false classified
    • ~ 2.8 % not classified
    Buildings Vegetation [Abbildung]
  • Results
  • Building Generalization
  • Results – Building Generalization
    • Building Objects are generalized by a bounding box
    • export as shape with attribute mean height
  • Performance Tests
    • Hardware
    • 1 Server HP DL380G4 , 4 CPUs
    • 1,8 Tb working storage
    • Software
    • 3 Definiens Server v7.0
    • 1 Definiens Developer v7.0
    • Processing Time
    • 1 processing unit (1000x1250m) = ~ 11min
    • 1 processing unit (1000x1250m) building generalization (v8.0 beta) = ~ 25min
  • Lessons learned
    • Classification quality depends essentially on the data quality:
    • accuracy of the geo-referencation
    • spectral quality of the optical data
    • filter quality of the LiDAR data
    • time lag between data acquisition (LiDAR and optical data)
    • Classification quality can be enhanced by the
    • usage of true orthofotos
    • usage of DTM, First- and Last Pulse data
  • 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]