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Special session „Bridging the gap with operations"

IIM Conference. Special session „Bridging the gap with operations". Case study on operational O bject- Based I mage A nalysis for Land Cover classification in security and emergency application Lukas Brodsky, Jan Kolomaznik, Tomas Bartalos lukas.brodsky@gisat.cz www.gisat.cz GISAT.

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Special session „Bridging the gap with operations"

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  1. IIM Conference Special session„Bridging the gap with operations" Case study on operational Object-BasedImage Analysis for Land Cover classification in security and emergency application Lukas Brodsky, Jan Kolomaznik, Tomas Bartalos lukas.brodsky@gisat.cz www.gisat.cz GISAT ESA-EUSC-JRC: Image Information Mining , "Bridging the gap with operations" November 4, 2009 , Madrid

  2. TASK Special session -> addressing and solving specific operational issues and to compare the results Motivation: • “EO sensors' variety, require new methodologies and tools for information mining and management, supported by shared knowledge” • “focus on automation in support of applications and services for geospatial intelligence “ IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  3. TASK GISAT goals of the case study: • demonstrate current status of the operational OBIA LC/LU classification and feature extraction • focus on the automation of the processing chain, while keeping standard level of product quality • „a standard“ = non-formal product specifications common to GMES Services as for instance applied in RESPOND project IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  4. INTRODUCTION • Security and emergency applications based on the EO image analysis User Requirements: • the usual request is time response • specific features extraction! • high quality product • product delivery in standardized data format and/or data structure = need of flexible, robust, fully automated or semi-automated image processing / Image Infromation Mining tools IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  5. INTRODUCTION • OBIA/GEOBIA approach „developing automated methods to partition remote sensing (RS) imagery into meaningful geographically based image-objects, and assessing their characteristics through spatial, spectral and temporal scales“ „GEOBIA requires image segmentation, attribution, classification and the ability to query and link individual objects (a.k.a. segments) in space and time“. Definiens Enterprise Image Intelligence™ Suite(Developer v. 7 + eCognition Server) IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  6. MATERIALS and METHODS • „Reference Data Set“ (EUSC data provided) QuickBird scene> MS + PAN + RGB pan-sharp IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  7. MATERIALS and METHODS • Data> auxiliary data set: Global Aster DEM IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  8. MATERIALS and METHODS • Classification nomenclature: • (Semi) Automatic Feature Extraction • Roads • Urbanized areas • Bodies of water • Linear features • Areas of vegetation • Man made features / structures • Desert area IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  9. MATERIALS and METHODS 25 classes selected and classified • Artificial (non military areas) • Built up areas (building blocks) • Damaged • Not damaged • Urban green • Gardens (backyards with mixed vegetation) • Grassland and commons (sparsely vegetated areas, cemeteries) • Parks (with high vegetation) • Roads • Bitumen roads • Consolidated / dirt roads • Path / trail • Airport • Construction sites • Mines • Dump sites • Pipelines • Military objects • Military areas • Trenches • Agricultural areas • Large and small fields, grassland fields • Small vegetation landscape units (between fields) • Bare soil areas (not cultivated fields) • Forest • Evergreen forest • Deciduous forest • Transitional woodland • Water • River • Lake • Canal • Flooded area IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  10. MATERIALS and METHODS • Processing chain • a/ image pre-processing • b/ image segmentation • c/ basic land cover classes detection • d/ segmentation + main land cover classes detection • e/ specific features extraction • f/ post-classification generalization • g/ GIS pos-classification analysis and feature detection • h/ visual inspection IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  11. MATERIALS and METHODS • Image pre-processing: PAN-SHARPENING • The automatic image fusion algorithm implemented in PCI Geomatica was applied (Zhang and Yun. 2002 ) - includes NIR channel! IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  12. MATERIALS and METHODS • Pre-processing • Road network was extracted to enhance the classification procedure Tested approaches: • a/ Semi-automatic: interactive digitizer • b/ PCI feature object tool (FOX) • c/ Hybrid method: image processing + semi-automated skeleton generation • d/ visual interpretation and digitizing = REFERENCE IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  13. MATERIALS and METHODS Road network derived from the image texture derivatives by semi-automated manner (automatic vectorization ArcScan) Comparison with reference data IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  14. MATERIALS and METHODS • Classification / feature extraction • hierarchical (multi-resolutional) segmentation and rule-base land cover classes detection # Objects labelling or describing semantic groups that represent knowledge about the image. Definiens cognition network IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  15. MATERIALS and METHODS • Image object hierarchy • Classes definition (class hierarchy) IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  16. MATERIALS and METHODS Classification Knowledge Base Rule-set IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  17. MATERIALS and METHODS • Classification: -> 1st Land Cover classes as a base -> 2nd specific feature extraction = potential of GIS analysis (interactions / relationship, etc.) IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  18. MATERIALS and METHODS • Feature extraction by OBIA: Small vegetation landscape units (hedgerows) IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  19. MATERIALS and METHODS • Feature extraction by OBIA: Trenches IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  20. MATERIALS and METHODS • Feature extraction by OBIA: damaged buildings (semi-automated procedure) IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  21. MATERIALS and METHODS • Post-classification utilization • Size generalization * MMU: 22 m2 * contextual classification / filtering • Shape generalization (Definiens Developer v. 7) * Mathematical morphology (closing, opening) IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  22. MATERIALS and METHODS • Shape generalization (eCognition v. 8) Example of rectangular buildings generalization (Definiens) IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  23. MATERIALS and METHODS • Post-classification utilization GIS post-classification analysis on Global ASTER DEM • detection of stream crossings • mean locations of bridges / fords IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  24. RESULTS and DISCUSSION • Result: Land Cover / Land Use classification and specific feature extraction was submitted as a vector data set IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  25. RESULT: a map IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  26. RESULTS and DISCUSSION • Processing chain: semi-automated • OBIA Land Cover / Land Use classification and specific feature extraction • Definiens Cognition Network Technology • rapid classification knowledge-base development with some level of transferability to other cases • importance of scale object-based mapping • Classes with limited number of examples: automation? or visual labelling of the objects! • GIS analysis: • Input data quality sensitive • Can be automated as well as interactive IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  27. RESULTS and DISCUSSION • Open issues for further development in automated procedure * road network extraction by automated approach = linear features detection in general! * performance in case of large very high resolution scenes MS: 4500 x 3500 pixel / lines (MB) PS: 20 000 x 15 000 pixel / lines (GB) * potential improvements is the shape generalisation methods IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

  28. Thank you for your attention! IIM 2009: "Bridging the gap with operations" November 4, 2009, Madrid

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