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CATENA Distributed Generic Processing Chain for Optical Satellite Imagery Processing

CATENA Distributed Generic Processing Chain for Optical Satellite Imagery Processing. Peter Reinartz, Thomas Krauß Remote Sensing Technology Institute Photogrammetry and Image Analysis ESA Workshop on Models for Scientific Exploitation of EO data Frascati, 2012-10-11.

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CATENA Distributed Generic Processing Chain for Optical Satellite Imagery Processing

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  1. CATENADistributed Generic Processing Chain for Optical Satellite Imagery Processing Peter Reinartz, Thomas Krauß Remote Sensing Technology InstitutePhotogrammetry and Image Analysis ESA Workshop on Models forScientific Exploitation of EO dataFrascati, 2012-10-11

  2. Why processing chains for higher level optical data processing? • Neededfor: • Processing of large areasand large datavolumese.g. Image2006/2009/2012, each time about 3500 scenesIRS/SPOT forwhole Europe-38 • Processing of time seriese.g. CCI-Fire, Meris/ATSR/SPOT-VGTfor 1995-2009, about 130.000 scenes • Requirements: • Fullyautomaticprocessingof • Massdatafrom • Manyopticalsensors/satellites • Modular and easy re-configurableformanyprojects Image 2006, ~3500IRS/SPOT scenes

  3. CATENA DEM Reference image Ortho image Original image CATENA – chain for fully automaticprocessing of optical satellite data Automatic and operational processing chain for processing ofmass data Using global databases and reference data Support of native satellite image formats from SPOT4/5, IRS-P6 Liss3/AWiFS, ALOS AVNIR/PRISM, Ikonos, Quickbird, RapidEye, WorldView, GeoEye, Cartosat, Pleiades, Meris, ATSR, VGT, Modis, … input output Image Matching Sensor Model Refinement Ortho-rectification Atmosphericcorrection remove atmospheric influence extract ground controlpoints from global Reference databases Use global DEM database perform parameter estimation Thematicprocessing

  4. CATENA CATENA – Use cases • Catena is • A chain of processing modules • Uses interface- and data standards • Usable as DIMS- or stand-alone-version • Example use cases are • Orthorectification in Image2006, Image2009, UrbanAtlas, ... • DEM-Generation as service for Cartosat/Euromap • Stereoprocessing, Time series, CCI-Fire, ...

  5. CATENA CATENA – Requirements • Systems and Libraries: • Linux (tested on CentOS, Ubuntu) • XDibias (DLR in-house development) • Python, scipy, numpy • GDAL • Modules: • Must not beinteractive (automaticprocessingchain!) • Preferable: UNIX C/C++ sourcecode, pythoncode • Possible: Java, Fortran, anystandard UNIX (script)programminglanguage • Nocommercialprogrammingenvironmentswhichrequireanykindoflicenses!

  6. CATENA CATENA – Interfaces • Input-Data: • Original Level-1-satellite datacontaining all metadata • Processeddataincludingrequiredmetadata • Modules: • Image dataandmetadata in standardizedXDibiasformat • Modules wrapexistingprocessorswithconfigurationfilesandanyimageformatsupportedby GDAL • Output: • Anyimageformatsupportedby GDAL • Standardized export.xml containingmeta- andprocessing info • JPG-Quicklooks, KML files, anyother intermediate files

  7. CATENA CATENA – Summary of Principles • Standardized image- and metadata formats • Standardized process flow organization • Processor follows ESA „Generic IPF Interface Specifications“ • Distributed computing and storage • Standardized Development and Deployment process • Guidelines for module development, documentation and deployment • ISO9000 certification in process: external audit today (2012-10-11)

  8. CATENA Originaldata ReferenceDB 1 ReferenceDB 2 ReferenceDB 3 Delivereddata Work-space CATENA – System overview Modules andorder definedin chain Some modulesneed additionaldata Selectprocessingchain and setparameters Import Processing chains Module 1 Module 2 Ingestion Module 3 Chain . . . Ortho Atmospheric Corr. DEM-Generation CCI-Fire Delivery Module 4 Module ... Export Standardizedimage andmeta data Processingcontrol system Web-Interface Each jobgets processed inown space Cleaned upafter delivery DIMS-PSM orstand-alone

  9. CATENA Referencedata DB CATENA – Grid computing • Simply add new node by creating working directory and inserting CATENA into crontab crontab work crontab work Node 2 crontab data Node 1 work crontab crontab . . . Node 3 work work Node 5 crontab Node 4 crontab work work crontab scenedatabase crontab work Server work Node 6 Node n webserver

  10. CATENA CATENA – Distributed Mass Storage • Distributed mass storage with access from each processing node is needed for automatic processing of time series or bulk data for: • Realized as easily expansible Scality storage ring: Data is storedautomatically inthree distributedcopies in the ring,read-access alsoin parallel fromthree storagenodes. Data

  11. CATENA Web-Interface of stand-alone version

  12. CATENA Examples • Orthorectificationgeocoded, optionallyatmosphericcorrectedsatelliteimagesforfurtherthematicprocessingandemergencymapping • DEM generationgenerate DEMs andOrthoimagesfrom (multi) stereosatellitedata

  13. CATENA Processing chain: Orthorectification • Standard processing chain for most optical satellite data • Satellites acquire oblique images • Ephemeris and attitude not exactly known • Correct these using ground control points • from already existing geocoded images • Project satellite image on existing digital elevation model • from DEM database (e.g. SRTM) • Resample satellite image in requested projection and resolution

  14. CATENA Processing chain: OrthorectificationWorkflow Original image Reference image DEM Matching Control points Improvementof orbit and attitude data Manually measured ground control points Generation of ortho image Delivery Quality check Ortho image Atmospheric Correction Thematic processing

  15. Coverage 2 Coverage 1 RMSE X Y X Y Overall Geometric Accuracy Requirement: RMSE < 20m Overall (~4000 scenes) mean accuracy w.r.t. reference data set: RMSEx/y ~ 10 m ( CE64 ~14m) ~0.5 pixel size of resampled images Mean number of ICPs per scene for accuracy assessment: IRS-P6: 5496 points / scene SPOT 4/5: 1360 points / scene Residual plots available

  16. Processing chain: DEM generationWorkflow Images • At least two images from same orbit • Good relative orientation required,<0.5px, Bundle block adjustment • Dense pixelwise Semi-Global Matching= Disparity map on original images • Reprojection of DEM to target coordiante system, Interpolation and filling of holes • Orthorectification of the original imagery Metadata Input Orientation Matching Processing DEM Generation Ortho-rectifi-cation Output DEM Ortho

  17. Processing chain: DEM generationOrtho image and DEM, London

  18. Processing chain: DEM generationLondon DSM from 5 WorldView-2 Images

  19. Very steep terrain Very detailed surface model Film: http://www.dlr.de/dlr/desktopdefault.aspx/tabid-10212/332_read-921/ Processing chain: DEM generationK2WorldView-2 Triple Stereo Processing Chains for Optical Data • Thomas Krauß • 2012-07-27 • www.DLR.de • Slide 19 15° 0° -15°

  20. CATENA Processing Chains for Optical DataSummary • Processing chain CATENA developed at the Remote Sensing Technology Institute of DLR for fully automatic processing of mass data from many different optical satellites • Already in use for many projects (Image2006-2012, UrbanAtlas, CCI-Fire, Cartosat-DEM-processor, Worldview-2 and Pleiades DEM generation, …) • Based on the general processing chain infrastructure CATENA including: • Modular system of processing Modules connected to Chains • Distributed parallel grid computing • Distributed mass storage • Easily expandable, e.g.: • A new processing chain for a new project • Adding normal Linux-PCs or virtual machines as new background processing nodes Contact: Thomas Krauß, DLR-IMF, Thomas.Krauss@dlr.de

  21. Thank you for your attention

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