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

Why processing chains for higher level optical data processing? Needed for: Processing of large areas and large data volumes e.g. Image2006/2009/2012, each time about 3500 scenes IRS/SPOT for whole Europe-38 Processing of time series e.g. CCI-Fire, Meris/ATSR/SPOT-VGT for 1995-2009, about 130.000 scenes Requirements: Fully automatic processing of Mass data from Many optical sensors/satellites Modular and easy re-configurable for many projects Image 2006, ~3500 IRS/SPOT scenes

Atmospheric correction CATENA CATENA – chain for fully automatic processing of optical satellite data Automatic and operational processing chain for processing of mass 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, … Reference image DEM input output Ortho image Original image Image Matching Sensor Model Refinement Ortho- rectification Atmospheric correction extract ground control points from global Reference databases perform parameter estimation Use global DEM database remove atmospheric influence Thematic processing

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, ...

CATENA CATENA – Requirements Systems and Libraries: Linux (tested on CentOS, Ubuntu) XDibias (DLR in-house development) Python, scipy, numpy GDAL Modules: Must not be interactive (automatic processing chain!) Preferable: UNIX C/C++ source code, python code Possible: Java, Fortran, any standard UNIX (script) programming language No commercial programming environments which require any kind of licenses!

CATENA CATENA – Interfaces Input-Data: Original Level-1-satellite data containing all metadata Processed data including required metadata Modules: Image data and metadata in standardized XDibias format Modules wrap existing processors with configuration files and any image format supported by GDAL Output: Any image format supported by GDAL Standardized export.xml containing meta- and processing info JPG-Quicklooks, KML files, any other intermediate files

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)

CATENA – System overview Modules and order defined in chain Some modules need additional data Select processing chain and set parameters Original data Import Processing chains Reference DB 1 Module 1 Module 2 Ingestion Reference DB 2 Module 3 Ortho DEM-Generation Atmospheric Corr. CCI-Fire Chain . . . Delivery Module 4 Reference DB 3 Delivered data Module ... Export Standardized image and meta data Work- space Processing control system Web-Interface Each job gets processed in own space Cleaned up after delivery DIMS-PSM or stand-alone

CATENA – Grid computing Reference data crontab crontab work work Node 2 crontab data Node 1 work crontab crontab Node 3 . . . work work Node 5 Node 4 crontab crontab work work scene database DB crontab work crontab Server work Simply add new node by creating working directory and inserting CATENA into crontab Node 6 Node n web server

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 stored automatically in three distributed copies in the ring, read-access also in parallel from three storage nodes. Data

Web-Interface of stand-alone version CATENA Web-Interface of stand-alone version

CATENA Examples Orthorectification geocoded, optionally atmospheric corrected satellite images for further thematic processing and emergency mapping DEM generation generate DEMs and Ortho images from (multi) stereo satellite data

Processing chain: Orthorectification 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

Processing chain: Orthorectification Workflow CATENA Processing chain: Orthorectification Workflow Original image Reference image Matching DEM Control points Improvement of orbit and attitude data Manually measured ground control points Generation of ortho image Quality check Delivery Probleme bei Qualität z.B. ungünstige Bewölkung, starke Änderungen von Referenz zu neuem Bild Prozessor generell auch für Flugzeugdaten geeignet BW: Diese Übersicht beschreibt auch gleichzeitig den Ablauf wie wir ihn für die SAR-GTC-Prozessierung anstreben. AP420 einzelne, neue Modulentwicklungen (Thema Passpunkte), AP430 Prozessketten Ortho image Atmospheric Correction Thematic processing

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 Coverage 1 Coverage 2 RMSE X Y X Y Mean number of ICPs per scene for accuracy assessment: IRS-P6: 5496 points / scene SPOT 4/5: 1360 points / scene Residual plots available

Processing chain: DEM generation Workflow Metadata Orientation Matching DEM Generation Ortho- rectifi- cation Images DEM Ortho Input Processing Output 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

Processing chain: DEM generation Ortho image and DEM, London

Processing chain: DEM generation London DSM from 5 WorldView-2 Images

Processing chain: DEM generation K2 WorldView-2 Triple Stereo Processing Chains for Optical Data • Thomas Krauß • 2012-07-27 • www.DLR.de • Slide 19 Processing chain: DEM generation K2 WorldView-2 Triple Stereo 15° Very steep terrain Very detailed surface model Film: http://www.dlr.de/dlr/desktopdefault.aspx/tabid-10212/332_read-921/ 0° -15°

Processing Chains for Optical Data Summary CATENA Processing Chains for Optical Data Summary 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

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