Satellite Image Classification - A Contextual Evidence based Approach

B AlMomani, SI McClean, PJ Morrow

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Remote sensing imaging techniques make use of data derived from high resolution satellite sensors. Image classification identifies and organises pixels of similar spatial distribution or similar statistical characteristics into the same spectral class (theme). Contextual data can be incorporated, or ‘fused’, with spectral data to improve the accuracy of classification algorithms. In this paper we use Dempster-Shafer’s theory of evidence to achieve data fusion. Incorporating a knowledge base of evidence within the classification process represents a new direction for the development of reliable systems for image classification and the interpretation of remotely sensed data.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages83-92
Number of pages10
Publication statusPublished - 7 Sep 2005
EventArtificial Intelligence and Cognitive Science 2005 -
Duration: 7 Sep 2005 → …

Conference

ConferenceArtificial Intelligence and Cognitive Science 2005
Period7/09/05 → …

Fingerprint

image classification
satellite sensor
pixel
satellite image
spatial distribution
remote sensing

Cite this

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title = "Satellite Image Classification - A Contextual Evidence based Approach",
abstract = "Remote sensing imaging techniques make use of data derived from high resolution satellite sensors. Image classification identifies and organises pixels of similar spatial distribution or similar statistical characteristics into the same spectral class (theme). Contextual data can be incorporated, or ‘fused’, with spectral data to improve the accuracy of classification algorithms. In this paper we use Dempster-Shafer’s theory of evidence to achieve data fusion. Incorporating a knowledge base of evidence within the classification process represents a new direction for the development of reliable systems for image classification and the interpretation of remotely sensed data.",
author = "B AlMomani and SI McClean and PJ Morrow",
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AlMomani, B, McClean, SI & Morrow, PJ 2005, Satellite Image Classification - A Contextual Evidence based Approach. in Unknown Host Publication. pp. 83-92, Artificial Intelligence and Cognitive Science 2005, 7/09/05.

Satellite Image Classification - A Contextual Evidence based Approach. / AlMomani, B; McClean, SI; Morrow, PJ.

Unknown Host Publication. 2005. p. 83-92.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Satellite Image Classification - A Contextual Evidence based Approach

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AU - McClean, SI

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PY - 2005/9/7

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N2 - Remote sensing imaging techniques make use of data derived from high resolution satellite sensors. Image classification identifies and organises pixels of similar spatial distribution or similar statistical characteristics into the same spectral class (theme). Contextual data can be incorporated, or ‘fused’, with spectral data to improve the accuracy of classification algorithms. In this paper we use Dempster-Shafer’s theory of evidence to achieve data fusion. Incorporating a knowledge base of evidence within the classification process represents a new direction for the development of reliable systems for image classification and the interpretation of remotely sensed data.

AB - Remote sensing imaging techniques make use of data derived from high resolution satellite sensors. Image classification identifies and organises pixels of similar spatial distribution or similar statistical characteristics into the same spectral class (theme). Contextual data can be incorporated, or ‘fused’, with spectral data to improve the accuracy of classification algorithms. In this paper we use Dempster-Shafer’s theory of evidence to achieve data fusion. Incorporating a knowledge base of evidence within the classification process represents a new direction for the development of reliable systems for image classification and the interpretation of remotely sensed data.

M3 - Conference contribution

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EP - 92

BT - Unknown Host Publication

ER -