An Introduction to ISO 19130 Imagery Sensor Models for Geopositioning - PowerPoint
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An Introduction to ISO 19130– Imagery Sensor
Models for Geopositioning
Liping Di
Center for Spatial Information Science and Systems (CSISS)
George Mason University
6301 Ivy Lane, Suite 620
Greenbelt, MD 20770
ldi@gmu.edu
CSISS
Briefing to CEOS WGISS on September 24, 2008 in Boulder, Colorado
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and Systems, George Mason University
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
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GCP Model
class Control Points (2)
MI_GeolocationInformation
Metadata for Imagery::MI_GCPCollection +controlPoints
+ collectionIdentification: Integer
0..1
+ collectionName: CharacterString
+ coordinateReferenceSystem: MD_ReferenceSystem
0..1
SD_ GCPRepository
+gcp 1.. *
+ accessRestricted: Boolean
Metadata for Imagery::MI_GCP + accessInformation: CI_Contact
+ geographicCoordinates: DirectPosition
constraints
{if self.accessRestricted = 'false'
then self.controlPoints->size = 1
else self.controlPoints->size >= 0
endif}
SD_ LocationGCP
SD_ ImageIdentifiableGCP
+ gridCoordinates: CV_GridCoordinates
+ description: CharacterString
SD_FittingFunction
SD_ GriddedGCPCollection
+ dimension: Integer
+ origin: DirectPosition
+ offsets: Sequence<Vector>
+ interpolation: CV_InterpolationMethod [0..1] CSISS Page 11
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Overview of Physical Sensor Model
class SD_PhysicalSensorModel ...
SD_PhysicalSensorModel
+ regionOfValidity: CV_GridPoint [3..*]
+platformInformation 1
SD_ PlatformParameters
+ dynamics: SD_PlatformDynamics [1..*]
+ offsetOf INS: Vector [ 0..1]
+ platformPosition: SD_Position
1 +sensorInformation
SD_ SensorParameters
+platformIdentification 1
Metadata for Imagery::MI_Platform
+ citation: CI_Citation [0..*]
+ identifier: MD_Identifier [0..*]
+ description: CharacterString
+ sponsor: CI_ResponsibleParty [ 0..*] CSISS Page 12
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Position
class SD_Position (10)
«DataType»
SD_ Position
+ navigationalConfidence: DQ_PositionalAccuracy [0..1]
constraints
{self.navigationalConfidence.nameOfMeasure = 'covarianceMatrix'}
«DataType» «DataType»
SD_EarthMeasuredLocation SD_OrbitMeasuredLocation
+ position: DirectPosition + 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}
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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_ SensorParameters
+ offsetAndOrientation: SD_PositionAndOrientation [1..*]]
+ referenceTime: DateTime [1..*]]]
+ operationalMode: CharacterString [0..1]]
constraints
Metadata for Imagery::MI_Instrument
{If self.identification.type='frame' or
+ citation: CI_Citation [0..*] self.identification.type='pushbroom' or
+ identifier: MD_Identifier self.identification.type='whiskbroom'
+ type: CharacterString then self.detector->size=1
+ description: CharacterString [0..1] else self.detector ->size=0 endif}
+systemAndOperation 1
SD_SensorSystemAndOperation
SD_ Sensor 1 + collectionStartTime: DateTime [0..1]
+ calibration: SD_Calibration [0..1] + collectionEndTime: DateTime [0..1]
+ mode: CharacterString [0..1] +identification
0..1 +gsdPropert ies
+ operationalBand: MI_Band [0..1]
«Data Type»
+detector 0..1 SD_ GSD
+ columnSpacing: Distance
SD_DetectorArray
+ rowSpacing: Distance
+ numberOfDimensions: Integer + gsdCRS: MD_ReferenceSyst em
+ arrayDimensions: Sequence<SD_ArrayDimension> + rangeIPR: Distance [ 0..1]
+ detectorShape: SD_ShapeCode [ 0..1] + azimuthIPR: SD_AzimuthMeasure [0..1]
+ detectorSize: Length + referenceSurface: SD_SurfaceCode
+ distortion: SD_Distortion
«CodeList» «Union»
«DataType» SD_ SurfaceCode SD_AzimuthMeasure
SD_ Calibration + ground + distance: Distance
+ inflatedEllipsoid + azimuth: Angle
+ calibrationAgency: CI_ResponsibleParty
+ calibrationDate: Date + ortho
+ slant
«DataType» «CodeList»
SD_ArrayDimension SD_ ShapeCode
+ name: CharacterString + circular
+ size: Integer + square
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Sensor System and Operation
class SD_SensorSystemAndOperation (15)
SD_SensorSystemAndOperation
+ collectionStartTime: DateTime [0..1]
+ collectionEndTime: DateTime [0..1]
SD_ Optics SD_Microw ave
SD_ OpticsOperation
+ instFieldOfView: Angle
+ scanDuration: TM_IntervalLength
+ swathFieldOfView: Angle
SD_SAROperation
+ orientation: SD_SAROrientationCode
SD_OpticalSystem + grpPosition: DirectPosition
+ calibratedFocalLength: Length
+ qualityOfFocalLength: DQ_QuantitativeAttributeAccuracy [0..1]
+ princPointAutoColl: DirectPosition «CodeList»
+ covPrincPtAutocoll: DQ_PositionalAccuracy [0..1] SD_SAROrientationCode
constraints + left
{self.qualityofFocallength.nameOfMeasure = 'variance'} + right
SD_ Distortion
+ princPointOfSymmetry: DirectPosition
+ qualityOfPrincPointOfSymmetry: DQ_QuantitativeAttributeAccuracy
constraints
{self.qualityofPrincPointOfSymmetry.nameOfMeasure = 'confidence interval'}
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Fitting Function
class SD_FittingFunction (18)
SD_ FittingFunction
SD_RationalPolynomial
+numerator 1
1
SD_Polynomial
+ resultDimension: MD_DimensionNameTypeCode +denominator
+coefficient 1.. *
SD_PolynomialCoefficient
+ value: Real
SD_V ariable
+variable + dimension: MD_DimensionNameTypeCode
+ power: Int eger
+ scaleFactor: Real [0..1]
0..* + translationValue: Real [0..1]
MI_GCPCollection
SD_ GridGCPCollection
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True Replacement Model
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Correspondence Model
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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 (ldi@gmu.edu, George Mason University)
• Co-Chair: Karen Moe (Karen.Moe@nasa.gov, NASA Goddard Space Flight
Center)
– Advances in Data Systems to Support Future Earth Observation
Missions
• Chair: Dr. Liping Di (ldi@gmu.edu, George Mason University, USA)
• Co-Chair: Ken McDonald (Kenneth.Mcdonald@noaa.gov, 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, Rama.Ramapriyan@nasa.gov
• Co-Chair: Francis Lindsay, NASA Headquarters, Washington, DC,
francis.lindsay-1@nasa.gov
• 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 (ldi@gmu.edu)
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