E Cognition User Summit2009 C Storch Gaf Emlc


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Euro-Maps Land Cover
The Definiens Enterprise Image Intelligence Suite for operational landcover map production

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E Cognition User Summit2009 C Storch Gaf Emlc

  1. 1. Euro-Maps Land Cover The Definiens Enterprise Image Intelligence Suite for operational landcover map production
  2. 2. Purpose Purpose <ul><li> Creation of an up-to-date landcover map of Germany for: </li></ul><ul><ul><ul><li>Tele-communication network planning </li></ul></ul></ul><ul><ul><ul><li>Environmental mapping / monitoring </li></ul></ul></ul><ul><ul><ul><li>Risk management </li></ul></ul></ul><ul><ul><ul><li>Regional planning </li></ul></ul></ul>
  3. 3. Product Specification GAF AG Coverage Germany Thematic Accuracy ≥ 95% per class Class Number 22 Positional Accuracy CE90 15m Ground Resolution 25m Base Satellite Data IRS-P6 LISS-III Minimum Object Size 0,25ha (2x2 pixel) Acquisition Year 2008-2006 Minimum Object Width 50m (2 pixel) Format Raster
  4. 4. Legend / Workflow Very Dense Urban Areas Dense Urban Areas Low-Density Urban Areas Very Low-Density Urban Areas Big buildings Impervious Surface Agriculture (Open) Water Shrub / Shrub-like Vegetation Wetland Vineyard Orchard Hop Rocks Excavated Material / Landfill Mining Fields Sand Bridges Greenhouses Coniferous Forest Mixed Forest Deciduous Forest Legend automatic image interpretation automatic
  5. 5. Results by Area [%]
  6. 6. Results by Area [%] Results by Area [%]
  7. 7. Data Specification <ul><li>IRS P6 LISS-III </li></ul><ul><li>43 scenes </li></ul><ul><li>23.5m spatial resolution </li></ul><ul><li>5 spectral bands: green, red, nir, swir + syn blue </li></ul><ul><li>Acquired mainly in 2007 and 2008 (May – Sep) </li></ul><ul><li>IRS-P6 LISS-IV Mono/LISS-III Merge </li></ul><ul><li>220 scenes </li></ul><ul><li>5m spatial resolution </li></ul><ul><li>4 spectral bands </li></ul><ul><li>Reference dates: 2005-2007 (Mar – Oct) </li></ul>Data Specification
  8. 8. Data Preparation <ul><li>220 scenes available </li></ul><ul><li>Original size ~ 1.5GB </li></ul>GAF AG <ul><li>After optimization: </li></ul><ul><ul><li>455 images (subscenes) </li></ul></ul><ul><ul><li>< 500 MB </li></ul></ul><ul><ul><li> 145 GB / 360.000 km² to process </li></ul></ul>
  9. 9. Classification Input <ul><li>Additional Input Data </li></ul><ul><li>Urban Area Mask </li></ul><ul><li>Metadata containing an image identifier and the acquisition date </li></ul><ul><li>The vector file containing the reference areas for each scene </li></ul>
  10. 10. Why eCognition? <ul><li>Why eCognition? </li></ul><ul><ul><li> For subsequent mapping, the main focus was on the delineation of the basic landcover types </li></ul></ul><ul><ul><li> results should be as homogeneous as possible </li></ul></ul><ul><ul><li> Definiens Developer provides very efficient workspace automation functions </li></ul></ul><ul><ul><li> Definiens Developer enables a very high degree of automation through the reuse of rulesets </li></ul></ul>GAF AG
  11. 11. Project Creation GAF AG
  12. 12. Ruleset Structure GAF AG
  13. 13. Subset Selection / Segmentation
  14. 14. Classification approach <ul><li>2 main steps: </li></ul><ul><li>Initial classification: </li></ul><ul><ul><li> use of only few features (ratios) for a robust but incomplete base classification </li></ul></ul><ul><ul><li> Computation of statistical parameters for each class </li></ul></ul><ul><li>Final classification: </li></ul><ul><ul><li> Use of absolute values in the final classification parameter sets </li></ul></ul>GAF AG
  15. 15. Classification Classification
  16. 16. Results Results
  17. 17. Classification Results GAF AG
  18. 18. Conclusion GAF AG <ul><li>Definiens Developer enables easy data management & organization </li></ul><ul><li>Workspace automation & ruleset recycling is very effective </li></ul><ul><li>Computational effort was immense </li></ul><ul><ul><li> Input of smaller subscenes </li></ul></ul>
  19. 19. Thanks for your attention !