E Cognition User Summit2009 Pregesbauer Geo Info Li Dar Basic Landcover


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Basic Landcover Classification by LiDAR and Optical Data

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  • state government provide the territorial communities data (lu lc) among other – unter anderem balance - Bilanz
  • E Cognition User Summit2009 Pregesbauer Geo Info Li Dar Basic Landcover

    1. 1. Basic Landcover Classification by LiDAR and Optical Data eCognition User Summit 2009 Munich November 2009
    2. 2. Contents <ul><li>Overview – data resources </li></ul><ul><li>Object Generation </li></ul><ul><li>Class Definition and Classification </li></ul><ul><li>Classification results </li></ul><ul><li>remarks on data accuracy and data precision </li></ul>
    3. 3. Why basic landcover classification for the public sector? <ul><li>legislative duties (e.g. land use planning, building regulation, sound wave propagation models) </li></ul><ul><li>planning purposes (e.g. infrastructure networks, soil sealing) </li></ul><ul><li>change detection (development of settlements) </li></ul>
    4. 4. Which Data for Classification <ul><li>Digital Orthofotos , 4 Channels (Red, Green, Blue, near Infrared), 12.5cm Ground Sampling Distance </li></ul><ul><li>Digital Terrain Model , 1m Grid width </li></ul><ul><li>Digital Surface Model , 1m Grid width </li></ul>DOP DSM DTM
    5. 5. Aim <ul><li>Classification of </li></ul><ul><li>Sealed areas </li></ul><ul><ul><li>Buildings </li></ul></ul><ul><ul><li>Road Network </li></ul></ul><ul><li>Vegetation (Forest Areas) </li></ul><ul><li>comprehensive data set for the whole state [20.000 km²] </li></ul><ul><li>stable classified classes (reliability ~ 95%) </li></ul>
    6. 6. Data processing <ul><li>Dataset Tiling (2000x2000 Pixel) </li></ul><ul><li>Creation of Initial Objects </li></ul><ul><li>Stitching to Object Primitives </li></ul><ul><li>Classification </li></ul>
    7. 7. Object Primitives by Image Object Fusion <ul><li>Object fusion based on a condition: </li></ul><ul><ul><li>spectral difference </li></ul></ul><ul><ul><li>height difference </li></ul></ul><ul><ul><li>border condition </li></ul></ul>Seed Candidate Candidate
    8. 8. Creation of Object Primitives <ul><li>The class filter allows restricting the potential candidates by their classification. </li></ul><ul><li>Fitting function threshold allows to select a feature and a condition you want to optimize the fusion. </li></ul><ul><li>Depending on the fitting mode , one or more candidates will be merged with the seed image object. </li></ul>
    9. 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
    10. 10. Improvement of Buildings
    11. 11. Appraisal of results <ul><li>Buildings </li></ul><ul><li>~ 88 % accurately classified </li></ul><ul><li>~ 9 % classified as elevated objects </li></ul><ul><li>~ 3 % not classified </li></ul><ul><li>Vegetation </li></ul><ul><li>~ 91 % accurately classified </li></ul><ul><li>~ 5 % false classified </li></ul><ul><li>~ 4% not classified </li></ul>Elevated Objects Buildings Vegetation
    12. 12. Misclassification - Example <ul><li>Building Objects within Vegetation Areas </li></ul><ul><li>Elevated Objects next to Buildings </li></ul>Elevated Objects Buildings Vegetation
    13. 13. Misclassification - Example <ul><li>Shadows </li></ul><ul><li>dark roofs </li></ul><ul><li>edges of buildings </li></ul><ul><li>lead to misclassifications at borders of buildings </li></ul>
    14. 14. Misclassification - Example <ul><li>Object Properties </li></ul><ul><li>Mean NDVI > 0 </li></ul><ul><li>Means nDS 4.7 m </li></ul><ul><li>Shadow Index > 0.07 </li></ul><ul><li>Object Class: Building </li></ul><ul><li>enhanced approach : usage of masks </li></ul>
    15. 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)
    16. 16. Classification improvement
    17. 17. Results <ul><li>Buildings </li></ul><ul><li>~ 94.3 % accurately classified </li></ul><ul><li>~ 5.2 % classified as elevated objects </li></ul><ul><li>~ 0.5 % not classified </li></ul><ul><li>Vegetation </li></ul><ul><li>~ 96.1% accurately classified </li></ul><ul><li>~ 1.1 % false classified </li></ul><ul><li>~ 2.8 % not classified </li></ul>Buildings Vegetation [Abbildung]
    18. 18. Results
    19. 19. Building Generalization
    20. 20. Results – Building Generalization <ul><li>Building Objects are generalized by a bounding box </li></ul><ul><li>export as shape with attribute mean height </li></ul>
    21. 21. Performance Tests <ul><li>Hardware </li></ul><ul><li>1 Server HP DL380G4 , 4 CPUs </li></ul><ul><li>1,8 Tb working storage </li></ul><ul><li>Software </li></ul><ul><li>3 Definiens Server v7.0 </li></ul><ul><li>1 Definiens Developer v7.0 </li></ul><ul><li>Processing Time </li></ul><ul><li>1 processing unit (1000x1250m) = ~ 11min </li></ul><ul><li>1 processing unit (1000x1250m) building generalization (v8.0 beta) = ~ 25min </li></ul>
    22. 22. Lessons learned <ul><li>Classification quality depends essentially on the data quality: </li></ul><ul><li>accuracy of the geo-referencation </li></ul><ul><li>spectral quality of the optical data </li></ul><ul><li>filter quality of the LiDAR data </li></ul><ul><li>time lag between data acquisition (LiDAR and optical data) </li></ul><ul><li>Classification quality can be enhanced by the </li></ul><ul><li>usage of true orthofotos </li></ul><ul><li>usage of DTM, First- and Last Pulse data </li></ul>
    23. 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]