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  • Challenge…. The currently sophisticated methods like parametric and geometric active contour models and texture measures are not efficient enough to be applied for near-real-time image processing and cannot be applied fully automatically. The only possibility here to keep up with the amount of daily incoming data during the TanDEM-X mission is to process the data fully automatically and to use a less time-consuming threshold method. In this case we apply the threshold method on….
  • … the amplitude image and on the coherence image as we can see here.
  • As you can see all water bodies are appearing smooth and dark without any disturbances caused by wind effect, snow and ice coverage or others
  • Hamburg, Nord-Ostsee-Kanal WAM erklären, wie WAM editing coherence low/amp dark filtering/Glättung
  • Hamburg, Nord-Ostsee-Kanal WAM erklären, wie WAM editing coherence low/amp dark filtering/Glättung


  • 1. Water Body Detection from TanDEM-X Data: concept & first evaluation of an accurate water indication mask A. Wendleder 1) , M. Breunig 1) , K. Martin 2) , B. Wessel 1) , A. Roth 1) 1) German Aerospace Center DLR | 2) Company for Remote Sensing and Environmental Research SLU IGARSS 2011 / Vancouver / 2011-07-28 IGARSS’11, Vancouver
  • 2. Outline
    • Introduction
    • Definition of the TanDEM-X water indication mask
    • Challenges for TanDEM-X water body detection
    • Concept & methodology of water body detection
    • Test site demonstration
    • Evaluation of classification results
    • Outlook
  • 3. Definition of the TanDEM-X water indication mask
    • Global mission – global DEM – global water body mask
    • Water body mask primarily extracted for post-processing DEM editing
      • ongoing work in flattening of outpoking water bodies
      • correct orthorectification of remote sensing data
    • No production of a complete global water body inventory
    Slide Kurnool Kadapa Channel / India frozen Lake Taimyr / Russia
  • 4. Challenges for TanDEM-X water body detection
    • TanDEM-X mission with 2 global acquisition data sets in 2011 & 2012
    • The water body detection runs completely data-driven
    • Processing at Raw DEM level (30*50 km ≈ 8.000*10.000 pixels)
    • 400 up to 800 Raw DEM per day to be processed
    • Therefore maximum computing time of 3 minutes per product
    • Applicable for different appearances of water bodies worldwide (coastline, inland lake, river, tropical, arctic, arid or humide climates etc.)
    Slide tropical river & coastline in Indonesia small inland water bodies in Minnesota / USA
  • 5. Concept & Methodology (I)
    • Input images are amplitude & coherence image
    • Exclusion of desert & polar regions
      • SRTM WAM
      • MODIS/Terra Land Cover Types
    • Exclusion of steep terrain
      • SRTM DEM
  • 6. Concept & Methodology (II) Slide
    • Median filter separately applied both to amplitude & coherence image
    • Threshold method with fix threshold values
      • Two different thresholds to handle complexity of water appearance
      • 1. threshold: reliable classification
      • 2. threshold: potential classification
    • Calculation of water body areas via Chain Code and elimination of water bodies < 1 hectare
    • Fusion of three intermediate water body layers
  • 7. Test site demonstration
    • River Elbe, Hamburg, Germany
    • acquired on January 27, 2011
    • Incidence angle 43.4° to 45.7°
  • 8. Evaluation of classification results (I) Slide
    • Calculation of completeness and correctness
    • reference vector layer data of digital landscape models from the Authoritative Topographic Cartographic Information System (ATKIS)
  • 9. Evaluation of classification results (II) Slide ATKIS: Authoritative Topographic Cartographic Information System Reference Completeness Correctness Amplitude ATKIS 86.9 % 92.6 % ATKIS water bodies > 1hectare 88.1 % 92.5 % Coherence ATKIS 79.8 % 98.7 % ATKIS water bodies > 1hectare 80.9 % 98.7 %
  • 10. Evaluation of classification results (III)
    • Water body mask derived of amplitude image
      • rich in detail
      • susceptible to misclassifications
    • Water body mask derived of coherence image
      • significant and robust results
      • loss of details of small scale water bodies
    • Maximum of a correct & complete water mask with combination of both
  • 11. Outlook
    • Accuracy assessment of the water body detection for different climate zones
      • robustness & global transferability of our approach
    • Mosaicking of different water bodies (neighboring acquisitions resp. first & second year acquisition) to an intermediate & final TanDEM-X water body mask product
    • TanDEM-X DEM editing using TanDEM-X water body mask
      • flattening of outpoking water bodies
  • 12. Slide River Elbe, Hamburg, Germany
    • SAR image
    • Water indication mask
    • DEM
    • edited DEM
  • 13. Slide
  • 14. Slide Thank you for your attention! Anna Wendleder | Markus Breunig German Remote Sensing Data Center Team SAR Topography Phone: +49 8153 28 3439 Email: |