Spatial Data Infrastructures: Extending Digital Image Processing Techniques and Management Models for Spatial Data Infrastructure Improvement

Brian Anthony Hope

Research output: ThesisDoctoral Thesis

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Abstract

combinatorial approach with existing image processing techniques and the application of Statistical Learning Machines (SLM) used successfully for object detection and classification. SDI metadata models have been extended to classify an organisational hierarchy of metadata standards with SDI metadata extensions to include temporal management applicable to imagery procurement discussed as well as planned and retired SDI raster management. SDI generations are discussed and client rendering models are introduced as a means of integrations vast amounts of imagery and raster data on the client side deployed from SDI services to improve Location Intelligence (LI) application for business users of spatial information and promote spatial information as Spatial Infrastructure.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Charles Sturt University
Place of PublicationAustralia
Publisher
Publication statusPublished - 2013
Externally publishedYes

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metadata
spatial data
infrastructure
raster
imagery
image processing
digital image processing technique

Cite this

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title = "Spatial Data Infrastructures: Extending Digital Image Processing Techniques and Management Models for Spatial Data Infrastructure Improvement",
abstract = "combinatorial approach with existing image processing techniques and the application of Statistical Learning Machines (SLM) used successfully for object detection and classification. SDI metadata models have been extended to classify an organisational hierarchy of metadata standards with SDI metadata extensions to include temporal management applicable to imagery procurement discussed as well as planned and retired SDI raster management. SDI generations are discussed and client rendering models are introduced as a means of integrations vast amounts of imagery and raster data on the client side deployed from SDI services to improve Location Intelligence (LI) application for business users of spatial information and promote spatial information as Spatial Infrastructure.",
author = "{Anthony Hope}, Brian",
note = "Hardcopy located at VC's Office-Bathurst. Not for Loan.",
year = "2013",
language = "English",
publisher = "Charles Sturt University",
address = "Australia",
school = "Charles Sturt University",

}

Spatial Data Infrastructures: Extending Digital Image Processing Techniques and Management Models for Spatial Data Infrastructure Improvement. / Anthony Hope, Brian.

Australia : Charles Sturt University, 2013.

Research output: ThesisDoctoral Thesis

TY - THES

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AU - Anthony Hope, Brian

N1 - Hardcopy located at VC's Office-Bathurst. Not for Loan.

PY - 2013

Y1 - 2013

N2 - combinatorial approach with existing image processing techniques and the application of Statistical Learning Machines (SLM) used successfully for object detection and classification. SDI metadata models have been extended to classify an organisational hierarchy of metadata standards with SDI metadata extensions to include temporal management applicable to imagery procurement discussed as well as planned and retired SDI raster management. SDI generations are discussed and client rendering models are introduced as a means of integrations vast amounts of imagery and raster data on the client side deployed from SDI services to improve Location Intelligence (LI) application for business users of spatial information and promote spatial information as Spatial Infrastructure.

AB - combinatorial approach with existing image processing techniques and the application of Statistical Learning Machines (SLM) used successfully for object detection and classification. SDI metadata models have been extended to classify an organisational hierarchy of metadata standards with SDI metadata extensions to include temporal management applicable to imagery procurement discussed as well as planned and retired SDI raster management. SDI generations are discussed and client rendering models are introduced as a means of integrations vast amounts of imagery and raster data on the client side deployed from SDI services to improve Location Intelligence (LI) application for business users of spatial information and promote spatial information as Spatial Infrastructure.

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