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E Cognition User Summit2009 C Storch Gaf Emlc
 

E Cognition User Summit2009 C Storch Gaf Emlc

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Euro-Maps Land Cover

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 E Cognition User Summit2009 C Storch Gaf Emlc Presentation Transcript

    • Euro-Maps Land Cover The Definiens Enterprise Image Intelligence Suite for operational landcover map production
    • Purpose Purpose
      •  Creation of an up-to-date landcover map of Germany for:
          • Tele-communication network planning
          • Environmental mapping / monitoring
          • Risk management
          • Regional planning
    • 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
    • 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
    • Results by Area [%]
    • Results by Area [%] Results by Area [%]
    • Data Specification
      • IRS P6 LISS-III
      • 43 scenes
      • 23.5m spatial resolution
      • 5 spectral bands: green, red, nir, swir + syn blue
      • Acquired mainly in 2007 and 2008 (May – Sep)
      • IRS-P6 LISS-IV Mono/LISS-III Merge
      • 220 scenes
      • 5m spatial resolution
      • 4 spectral bands
      • Reference dates: 2005-2007 (Mar – Oct)
      Data Specification
    • Data Preparation
      • 220 scenes available
      • Original size ~ 1.5GB
      GAF AG
      • After optimization:
        • 455 images (subscenes)
        • < 500 MB
        •  145 GB / 360.000 km² to process
    • Classification Input
      • Additional Input Data
      • Urban Area Mask
      • Metadata containing an image identifier and the acquisition date
      • The vector file containing the reference areas for each scene
    • Why eCognition?
      • Why eCognition?
        •  For subsequent mapping, the main focus was on the delineation of the basic landcover types
        •  results should be as homogeneous as possible
        •  Definiens Developer provides very efficient workspace automation functions
        •  Definiens Developer enables a very high degree of automation through the reuse of rulesets
      GAF AG
    • Project Creation GAF AG
    • Ruleset Structure GAF AG
    • Subset Selection / Segmentation
    • Classification approach
      • 2 main steps:
      • Initial classification:
        •  use of only few features (ratios) for a robust but incomplete base classification
        •  Computation of statistical parameters for each class
      • Final classification:
        •  Use of absolute values in the final classification parameter sets
      GAF AG
    • Classification Classification
    • Results Results
    • Classification Results GAF AG
    • Conclusion GAF AG
      • Definiens Developer enables easy data management & organization
      • Workspace automation & ruleset recycling is very effective
      • Computational effort was immense
        •  Input of smaller subscenes
    • Thanks for your attention !