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CSISS

Center for Spatial Information Science and Systems, George Mason University

An Introduction to ISO 19130– Imagery Sensor Models for Geopositioning

Briefing to CEOS WGISS on September 24, 2008 in Boulder, Colorado

Liping Di

Center for Spatial Information Science and Systems (CSISS)

George Mason University

6301 Ivy Lane, Suite 620

Greenbelt, MD 20770

[email protected]

CSISS


Contents

• Briefing on ISO 19130 project– The scope of ISO 19130– Background and history– Schedule – Contents of the latest version of standard

• A invitation for CEOS WGISS to contribute

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Introduction

• Numerous sensors are collecting data. – Geopositioning is a fundamental processing step before the data become

useful.

• Current situation: the diversity of sensor types and the lack of a common sensor model standard. Data from different producers

– contain different parametric information– lack parameters required to describe the sensor that produces the data– lack ancillary data necessary for geopositioning and analyzing the data.

• The problem– A separate software package often has to be developed to deal with data

from each individual sensor or data producer.– Unable to archive the plug-in-and-play capability sought by the constellation

and sensor web concepts.

• The solution: Standard sensor models and geolocation metadata – Allow the development of generalized geopositioning software. – Promote interoperability of data between sensors– facilitate data exchange.

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What is ISO 19130

• Identifies the information required to determine the relationship between the position of a remotely sensed pixel in image coordinates and its geoposition.

• Defines the metadata to be distributed with the image to enable user determination of geographic position from the observations.

• ISO 19130 specifies four ways in which geolocation information may be provided.

– A sensor description with the associated physical and geometric information necessary to rigorously construct a Physical Sensor Model.

• General sensor model components• Detail models for frame, pushbroom, whiskbroom, and SAR sensors, constructed

from those components as examples. – A True Replacement Model, using functions whose coefficients are based

on a Physical Sensor Model. – A Correspondence Model that provides a functional fitting based on

observed relationships between the geopositions of a set of ground control points and their image coordinates.

– A set of Ground Control Points that can be used to develop a Correspondence Model or to refine a Physical Sensor Model or True Replacement Model.

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History

• Started in March 2001– 20 experts from 12 countries and 3 international organizations formed

the project team

– Chaired by Liping Di of U.S.

• Removed from ISO TC 211 program of work in March 2006 due to funding issue.

• Reintroduced into ISO TC 211 program of work in February 2008 by U.S. with additional sensor types covered.– After long preparation work by U.S. INCITS L1 sensor modeling group

chaired by Liping Di of GMU and Bill Craig of NGA/SeiCorp

– Progress very rapidly since the reintroduction

• Currently an ISO 19130 document is being voted by the members of ISO TC 211 members as ISO Draft Technical Specification (ISO DTS)

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ISO 19130 Project Schedule

• 1 Aug 08: ISO/TC 211 issues DTS inquiry for 3 month ballot.• 31 Oct 08: Ballot closed.• 01-03 Dec 08: 19130 Editing Committee Meeting to adjudicate

comments (in conjunction with TC 211 Plenary) in Japan • January 2009: Edited DTS to ISO TC 211. • May 2009: DTS published as TS at ISO’s convenience.

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The 19130 DTS Draft Version

• The current 19130 DTS draft includes:– 8 clauses– 3 normative annexes– 3 informative annexes– An introduction clause

• The draft currently has 164 pages

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Geopositioning, Geolocating, and Georeferencing

• Geopositioning: determining the ground coordinates of an object from image coordinates.

• Geolocating: geopositioning an object using a sensor model

• Georeferencing: geopositioning an object using a correspondence model derived from a set of points for which both ground and image coordinates are known.

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The Table of Contents of 19130

• Introduction• 1 Scope• 2 Conformance• 3 Normative references• 4 Terms and definitions• 5 Symbols and abbreviated terms• 6 Image geopositioning: overview and common elements • 7 Physical Sensor Model• 8 True replacement models and correspondence models

• Annex A Conformance and testing (normative)• Annex B Geolocation information data dictionary (normative)• Annex C Coordinate systems (normative)• Annex D Frame sensor model metadata profile supporting precise

geopositioning (informative)• Annex E Pushbroom / Whiskbroom sensor model metadata profile

(informative)• Annex F Synthetic Apeture Radar sensor model metadata profile

supporting precise geopositioning (informative)

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Top Level UML Model

class SD_SensorMo...

SD_PhysicalSensorModel SD_TrueReplacementModel

SD_SensorModel

+ forImageID: CharacterString

SD_CorrespondenceModel

{XOR}

«Abstract»Metadata for Imagery::

MI_GeolocationInformation

Metadata for Imagery::MI_GCPCollection

+ collectionIdentification: Integer+ collectionName: CharacterString+ coordinateReferenceSystem: MD_ReferenceSystem

+correspondenceModel1.. *+trueReplacementModel1.. *+physicalSensorModel 1.. *

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GCP Modelclass Control Points (2)



+ collectionIdentification: Integer+ collectionName: CharacterString+ coordinateReferenceSystem: MD_ReferenceSystem

Metadata for Imagery::MI_GCP

+ geographicCoordinates: DirectPosition

SD_ LocationGCP

+ gridCoordinates: CV_GridCoordinatesSD_ ImageIdentifiableGCP

+ description: CharacterString

SD_ GCPRepository

+ accessRestricted: Boolean+ accessInformation: CI_Contact

constraints{if self.accessRestricted = 'false' then self.controlPoints->size = 1 else self.controlPoints->size >= 0 endif}

SD_FittingFunction

SD_ GriddedGCPCollection

+ dimension: Integer+ origin: DirectPosition+ offsets: Sequence<Vector>+ interpolation: CV_InterpolationMethod [0..1]

+controlPoints

0..1

0..1

+gcp 1.. *

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Overview of Physical Sensor Model

class SD_PhysicalSensorModel ...

SD_PhysicalSensorModel

+ regionOfValidity: CV_GridPoint [3..*]

SD_PlatformParameters

+ dynamics: SD_PlatformDynamics [1..*]+ offsetOf INS: Vector [0..1]+ platformPosition: SD_Position

SD_SensorParameters

Metadata for Imagery::MI_Platform

+ citation: CI_Citation [0..*]+ identifier: MD_Identifier [0..*]+ description: CharacterString+ sponsor: CI_ResponsibleParty [0..*]

+sensorInformation1

+platformInformation 1

+platformIdentification 1

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Position

class SD_Position (10)

«DataType»SD_Position

+ navigationalConfidence: DQ_PositionalAccuracy [0..1]

constraints{self.navigationalConfidence.nameOfMeasure = 'covarianceMatrix'}

«DataType»SD_OrbitMeasuredLocation

+ argumentOfPerigee: Angle+ bStarDrag: Real [0..1]+ eccentricity: Real+ epoch: DateTime+ inclination: Angle+ meanAnomaly: Angle [0..1]+ meanMotion: Real+ perigeePassageTime: DateTime [0..1]+ period: TM_Duration [0..1]+ referenceCRS: SC_CRS+ revNumber: Integer [0..1]+ rightAscensionAscendingNode: Angle+ semiMajorAxis: Length [0..1]

constraints{(self.meanMotion->size + self.period->size + self.semimajorAxis ->size) >=1}{(self.meanAnomaly->size + self.perigeePassTime->size) >=1}

«DataType»SD_EarthMeasuredLocation

+ position: DirectPosition

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Dynamics

class SD_Dynamics (12)

SD_Dynamics

+ attitude: SD_Attitude [0..1]+ velocity: Velocity [0..1]+ acceleration: Acceleration [0..*]+ angularVelocity: AngularVelocity [0..1]+ angularAcceleration: AngularAcceleration [0..1]+ dateTime: DateTime

SD_PlatformDynamics

+ trueHeading: Angle [0..1]+ yaw: Angle [0..1]

constraints{(self.attitude->size + self.yaw->size) = 1}{(self.velocity->size + self.trueHeading->size) <=1}

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Sensor Parameters

class SD_SensorParameters (14)

SD_DetectorArray

+ numberOfDimensions: Integer+ arrayDimensions: Sequence<SD_ArrayDimension>+ detectorShape: SD_ShapeCode [0..1]+ detectorSize: Length+ distortion: SD_Distortion

SD_SensorParameters

+ offsetAndOrientation: SD_PositionAndOrientation [1..*]]+ referenceTime: DateTime [1..*]]]+ operationalMode: CharacterString [0..1]]

constraints{If self.identification.type='frame' or self.identification.type='pushbroom' or self.identification.type='whiskbroom' then self.detector->size=1 else self.detector ->size=0 endif}

SD_SensorSystemAndOperation

+ collectionStartTime: DateTime [0..1]+ collectionEndTime: DateTime [0..1]

«Data Type»SD_GSD

+ columnSpacing: Distance+ rowSpacing: Distance+ gsdCRS: MD_ReferenceSystem+ rangeIPR: Distance [0..1]+ azimuthIPR: SD_AzimuthMeasure [0..1]+ referenceSurface: SD_SurfaceCode

SD_Sensor

+ calibration: SD_Calibration [0..1]+ mode: CharacterString [0..1]+ operationalBand: MI_Band [0..1]

«Union»SD_AzimuthMeasure

+ distance: Distance+ azimuth: Angle

«DataType»SD_Calibration

+ calibrationAgency: CI_ResponsibleParty+ calibrationDate: Date

«CodeList»SD_ShapeCode

+ circular+ square

«CodeList»SD_SurfaceCode

+ ground+ inflatedEllipsoid+ ortho+ slant

Metadata for Imagery::MI_Instrument

+ citation: CI_Citation [0..*]+ identifier: MD_Identifier+ type: CharacterString+ description: CharacterString [0..1]

«DataType»SD_ArrayDimension

+ name: CharacterString+ size: Integer

+identification

1

+systemAndOperation 1

+detector 0..1

+gsdPropert ies0..1

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Sensor System and Operationclass SD_SensorSystemAndOperation (15)

SD_SensorSystemAndOperation

+ collectionStartTime: DateTime [0..1]+ collectionEndTime: DateTime [0..1]

SD_Optics SD_Microwave

SD_OpticsOperation

+ instFieldOfView: Angle+ scanDuration: TM_IntervalLength+ swathFieldOfView: Angle

SD_OpticalSystem

+ calibratedFocalLength: Length+ qualityOfFocalLength: DQ_QuantitativeAttributeAccuracy [0..1]+ princPointAutoColl: DirectPosition+ covPrincPtAutocoll: DQ_PositionalAccuracy [0..1]

constraints{self.qualityofFocallength.nameOfMeasure = 'variance'}

SD_SAROperation

+ orientation: SD_SAROrientationCode+ grpPosition: DirectPosition

«CodeList»SD_SAROrientationCode

+ left+ right

SD_Distortion

+ princPointOfSymmetry: DirectPosition+ qualityOfPrincPointOfSymmetry: DQ_QuantitativeAttributeAccuracy

constraints{self.qualityofPrincPointOfSymmetry.nameOfMeasure = 'confidence interval'}

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Fitting Functionclass SD_FittingFunction (18)

SD_ FittingFunction

SD_Polynomial

+ resultDimension: MD_DimensionNameTypeCode

SD_RationalPolynomial

SD_PolynomialCoefficient

+ value: RealSD_Variable

+ dimension: MD_DimensionNameTypeCode+ power: Integer+ scaleFactor: Real [0..1]+ translationValue: Real [0..1]

MI_GCPCollection

SD_ GridGCPCollection

+variable

0..*

+coefficient 1.. *

+denominator

1

+numerator 1

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True Replacement Model

class SD_TrueReplacementModel (20)

SD_TrueReplacementModel

+ regionOfValidity: CV_GridPoint [3..*]+ fittingFunction: SD_FittingFunction+ accuracy: DQ_PositionalAccuracy

constraints{self.accuracy.nameOfMeasure = 'covariance matrix'}

SD_PhysicalSensorModel

«derive»

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Correspondence Model

class SD_CorrespondenceModel (...

SD_CorrespondenceModel

+ regionOfValidity: CV_GridPoint [3..*]+ fittingFunction: SD_FittingFunction



SD_ GCPRepository

{XOR}

+controlPoints 0..1

+controlPoints

0..1

0..1+repositoryGCP 1

«derive»

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How Can CEOS Contribute to 19130

• Review current draft DTS– provide any comments in ISO format– Nominate an expert to the 19130 Editing

Committee as the WGISS representative. – submit the comments and nomination to ISO TC

211 secretariat through WGISS liaison to ISO TC 211

• Due date for the submission- Oct 31, 2008

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What is Next in ISO TC 211

• ISO standard on Calibration and Validation of Remote Sensing Data– We are looking for funding support to start this project.

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IGARSS 2009 DADTC Special Invited Sessions

• Cape Town, South Africa July 13-17.• The Data Archiving and Distribution Technical Committee of IEEE

GRSS is organizing three invited sessions– Earth Observation Sensor Web

• Chair: Dr. Liping Di ([email protected], George Mason University)• Co-Chair: Karen Moe ([email protected], NASA Goddard Space Flight

Center)

– Advances in Data Systems to Support Future Earth Observation Missions

• Chair: Dr. Liping Di ([email protected], George Mason University, USA)• Co-Chair: Ken McDonald ([email protected], National Oceanic and

Atmospheric Administration, USA )

– Progress in Use of Web Services to Enhance Earth Science Research in a Highly Distributed Environment

• Chair: Hampapuram K. Ramapriyan (Rama), NASA Goddard Space Flight Center, Greenbelt, MD, [email protected]

• Co-Chair: Francis Lindsay, NASA Headquarters, Washington, DC, [email protected]

• Welcome WGISS members to contribute papers– Please contact chair or co-chair of the sessions for your contributions.

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17th International Conference on Geoinformatics

• Annual Conference since 1992.• 17th conference on August 12-14, 2009 in Fairfax, VA, organized by George Mason

University (http://www.geoinformatics2009.org)• Themes include but not limited to

– Earth observation technology, data systems, and applications– Geospatial Semantic Web, Sensor Web, Grid, and Web Portal– Geospatial Web Services and service quality, and Workflow-oriented geospatial

decision support system,– Virtual Globes and their application to scientific research and daily life– Geospatial Interoperability and standards– Automated object extraction and database updates from imagery– Integration of RS, GIS and GPS (3S)– LIDAR technology for DEM generation and 3D modeling– Geospatial education, such as virtual globes-based virtual education– Acquisition and processing of Remotely Sensed Data– Information Extraction from Remotely Sensed Data– Theories and Algorithms in GIS– Climate Changes and Global Environment – Global Earth Observation System of Systems (GEOSS)

• Abstract due: Jan. 30th, 2009. Papers will be published as IEEE proceedings and selected papers will be published in peer-reviewed journals

• Welcome CEOS WGISS members to organize sessions and contribute papers. • Contact Liping Di ([email protected])

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