catena distributed generic processing chain for optical satellite imagery processing peter reinartz,...

CATENADistributed Generic Processing Chain for Optical Satellite Imagery Processing

Peter Reinartz, Thomas KraußRemote Sensing Technology InstitutePhotogrammetry and Image Analysis

ESA Workshop on Models forScientific Exploitation of EO data

Frascati, 2012-10-11

Processing Chains for Optical Data • Thomas Krauß • 2012-10-11 • www.DLR.de • Slide 2

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 scenesIRS/SPOT for whole Europe-38

• Processing of time seriese.g. CCI-Fire, Meris/ATSR/SPOT-VGTfor 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 projectsImage 2006, ~3500IRS/SPOT scenes


ImageMatching

Sensor ModelRefinement

Ortho-rectification

Atmosphericcorrection

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, …

Thematicprocessing

input output

extract ground controlpoints from globalReference databases

perform parameterestimation

Use globalDEM database

removeatmosphericinfluence

Reference image

Original image

Ortho image

DEM

CATENACATENACATENA – chain for fully automaticprocessing of optical satellite data


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, ...

CATENACATENA


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!

CATENACATENA


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

CATENACATENA


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)

CATENACATENA


Work-space

CATENACATENA

Module 1

Originaldata

ReferenceDB 3

Ingestion

Ort

ho

Web-Interface

ReferenceDB 2

ReferenceDB 1Module 2

Module 3

Module 4

Export

Import

Module ...DE

M-G

ener

atio

n

CC

I-F

ire

Ch

ain

. . .

Delivery

Delivereddata

Processingcontrol system

Selectprocessing

chain and setparameters

Selectprocessing

chain and setparameters

Modules andorder defined

in chain

Modules andorder defined

in chainSome modulesneed additional

data

Some modulesneed additional

data

Each jobgets processed in

own space

Each jobgets processed in

own space

Standardizedimage andmeta data

Standardizedimage andmeta data

DIMS-PSM orstand-alone

DIMS-PSM orstand-alone

Cleaned upafter delivery

Cleaned upafter delivery

Processing chains

Atm

osp

her

ic C

orr

.

CATENA – System overview


CATENA – Grid computing

Referencedata

data

crontab

work

crontab

work

crontab

work

crontab

work

crontab

work

crontab

work

crontab

work

crontab

work . . .

Node n

Node 1

Node 2

Node 5 Node 4

Node 3

Node 6

Server

crontab

work

DB

webserver

scenedatabase • Simply add new node

by creating working directory and inserting CATENA into crontab

CATENACATENA


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:

CATENACATENA

Data

Data is storedautomatically inthree distributedcopies in the ring,read-access alsoin parallel fromthree storagenodes.


Web-Interface of stand-alone version

CATENACATENA


Examples

• Orthorectificationgeocoded, optionally atmospheric corrected satellite images for further thematic processing and emergency mapping

• DEM generationgenerate DEMs and Ortho images from (multi) stereo satellite data

CATENACATENA


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

CATENACATENA


Processing chain: OrthorectificationWorkflow

CATENACATENA

Original image Reference image

DEM

Ortho image

Matching

Control points

Improvementof orbit and attitude data

Generation of ortho image

Quality check

Manually measuredground control points

Delivery

Atmospheric Correction

Thematic processing


RMSE x (blue) and RMSE y (red) Coverage 1&2

0

5

10

15

20

met

er

Overall Geometric AccuracyOverall Geometric Accuracy

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 / sceneSPOT 4/5: 1360 points / sceneResidual plots available

Cov

erag

e 1

Cov

erag

e 2

RMSE X Y X Y

Requirement: RMSE < 20m


• 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

Orientation

Matching

DEM Generation

Ortho-rectifi-cation

Images

DEM Ortho

Inp

ut

Pro

cess

ing

Ou

tpu

tProcessing chain: DEM generationWorkflow


Processing chain: DEM generationOrtho image and DEM, London


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

• Very steep terrain

• Very detailed surface model• Film: http://www.dlr.de/dlr/desktopdefault.aspx/tabid-10212/332_read-921/

15°

0°

-15°

Processing chain: DEM generationK2 WorldView-2 Triple Stereo


http://www.dlr.de/dlr/desktopdefault.aspx/tabid-10212/332_read-921/


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

CATENACATENA

Contact: Thomas Krauß, DLR-IMF, [email protected]


Thank you for your attention

catena distributed generic processing chain for optical satellite imagery processing peter reinartz,...

Documents

image data

processing chains

optical data thomas

chain of processing

operational processing

generic processing chain

processing of time series

processing of large