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<meta content="Welch, Samuel J" name="eprints.creators_name" />
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<meta content="Interactive visualisation techniques for data mining of satellite imagery" name="eprints.title" />
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<meta content=""visual data mining", VDM, remote sesning, heard island, feature space, isosurfaces, alphashapes, visualisation" name="eprints.keywords" />
<meta content="Supervised classification of satellite imagery largely removes the user from the information extraction process. Visualisation is an often ignored means by which users may interactively explore the complex patterns and relationships in satellite imagery. Classification can be considered a 'hypothesis testing' form of analysis. Visual Data Mining allows for dynamic hypothesis generation, testing and revision based on a human user's perception. In this study Visual Data Mining was applied to the classification of satellite imagery.
After reviewing appropriate techniques and literature a tool was developed for the visual exploration and mining of satellite image data. This tool augments existing semi-automatic data mining techniques with visualisation capabilities. The tool was developed in IDL as an extension to ENVI, a popular remote sensing package.
The tool developed was used to conduct a visual data mining analysis of high-resolution imagery of Heard Island. This process demonstrated the positive impacts of visualisation and visual data mining when used in the analysis of satellite imagery. These impacts consist of: increased opportunity for understanding and hence confidence in classification results, increased opportunity for the discovery of subtle patterns in satellite imagery and the ability to create, test and revise hypotheses based on visual assessment. " name="eprints.abstract" />
<meta content="2006-11" name="eprints.date" />
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<meta content="University of Tasmania" name="eprints.institution" />
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<meta content="Abdi, H 2007 (in-press), 'Eigen-decomposition: eigenvalues and eigenvecteurs.' in NJ Salkind (ed.), Encyclopedia of Measurement and Statistics, Sage, Thousand Oaks (CA).
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---- 2006, Heard Island Dataset, Regions of Interest to S Welch, H Murray & R Williams.
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<meta content="Supervised classification of satellite imagery largely removes the user from the information extraction process. Visualisation is an often ignored means by which users may interactively explore the complex patterns and relationships in satellite imagery. Classification can be considered a 'hypothesis testing' form of analysis. Visual Data Mining allows for dynamic hypothesis generation, testing and revision based on a human user's perception. In this study Visual Data Mining was applied to the classification of satellite imagery.
After reviewing appropriate techniques and literature a tool was developed for the visual exploration and mining of satellite image data. This tool augments existing semi-automatic data mining techniques with visualisation capabilities. The tool was developed in IDL as an extension to ENVI, a popular remote sensing package.
The tool developed was used to conduct a visual data mining analysis of high-resolution imagery of Heard Island. This process demonstrated the positive impacts of visualisation and visual data mining when used in the analysis of satellite imagery. These impacts consist of: increased opportunity for understanding and hence confidence in classification results, increased opportunity for the discovery of subtle patterns in satellite imagery and the ability to create, test and revise hypotheses based on visual assessment. " name="DC.description" />
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<h1 class="ep_tm_pagetitle">Interactive visualisation techniques for data mining of satellite imagery</h1>
<p style="margin-bottom: 1em" class="not_ep_block"><span class="person_name">Welch, Samuel J</span> (2006) <xhtml:em>Interactive visualisation techniques for data mining of satellite imagery.</xhtml:em> Honours thesis, University of Tasmania.</p><p style="margin-bottom: 1em" class="not_ep_block"></p><table style="margin-bottom: 1em" class="not_ep_block"><tr><td valign="top" style="text-align:center"><a onmouseover="EPJS_ShowPreview( event, 'doc_preview_969' );" href="http://eprints.utas.edu.au/933/1/front_matter_welch.pdf" onmouseout="EPJS_HidePreview( event, 'doc_preview_969' );"><img alt="[img]" src="http://eprints.utas.edu.au/style/images/fileicons/application_pdf.png" class="ep_doc_icon" border="0" /></a><div class="ep_preview" id="doc_preview_969"><table><tr><td><img alt="" src="http://eprints.utas.edu.au/933/thumbnails/1/preview.png" class="ep_preview_image" border="0" /><div class="ep_preview_title">Preview</div></td></tr></table></div></td><td valign="top"><a href="http://eprints.utas.edu.au/933/1/front_matter_welch.pdf"><span class="ep_document_citation">PDF (Front Matter)</span></a> - Requires a PDF viewer<br />96Kb</td></tr><tr><td valign="top" style="text-align:center"><a onmouseover="EPJS_ShowPreview( event, 'doc_preview_970' );" href="http://eprints.utas.edu.au/933/2/welch2006.pdf" onmouseout="EPJS_HidePreview( event, 'doc_preview_970' );"><img alt="[img]" src="http://eprints.utas.edu.au/style/images/fileicons/application_pdf.png" class="ep_doc_icon" border="0" /></a><div class="ep_preview" id="doc_preview_970"><table><tr><td><img alt="" src="http://eprints.utas.edu.au/933/thumbnails/2/preview.png" class="ep_preview_image" border="0" /><div class="ep_preview_title">Preview</div></td></tr></table></div></td><td valign="top"><a href="http://eprints.utas.edu.au/933/2/welch2006.pdf"><span class="ep_document_citation">PDF (Whole Thesis)</span></a> - Requires a PDF viewer<br />7Mb</td></tr></table><div class="not_ep_block"><h2>Abstract</h2><p style="padding-bottom: 16px; text-align: left; margin: 1em auto 0em auto">Supervised classification of satellite imagery largely removes the user from the information extraction process. Visualisation is an often ignored means by which users may interactively explore the complex patterns and relationships in satellite imagery. Classification can be considered a 'hypothesis testing' form of analysis. Visual Data Mining allows for dynamic hypothesis generation, testing and revision based on a human user's perception. In this study Visual Data Mining was applied to the classification of satellite imagery.
After reviewing appropriate techniques and literature a tool was developed for the visual exploration and mining of satellite image data. This tool augments existing semi-automatic data mining techniques with visualisation capabilities. The tool was developed in IDL as an extension to ENVI, a popular remote sensing package.
The tool developed was used to conduct a visual data mining analysis of high-resolution imagery of Heard Island. This process demonstrated the positive impacts of visualisation and visual data mining when used in the analysis of satellite imagery. These impacts consist of: increased opportunity for understanding and hence confidence in classification results, increased opportunity for the discovery of subtle patterns in satellite imagery and the ability to create, test and revise hypotheses based on visual assessment. </p></div><table style="margin-bottom: 1em" cellpadding="3" class="not_ep_block" border="0"><tr><th valign="top" class="ep_row">Item Type:</th><td valign="top" class="ep_row">Thesis (Honours)</td></tr><tr><th valign="top" class="ep_row">Keywords:</th><td valign="top" class="ep_row">"visual data mining", VDM, remote sesning, heard island, feature space, isosurfaces, alphashapes, visualisation</td></tr><tr><th valign="top" class="ep_row">Subjects:</th><td valign="top" class="ep_row"><a href="http://eprints.utas.edu.au/view/subjects/291003.html">290000 Engineering and Technology > 291000 Geomatic Engineering > 291003 Photogrammetry and Remote Sensing</a><br /><a href="http://eprints.utas.edu.au/view/subjects/280399.html">280000 Information, Computing and Communication Sciences > 280300 Computer Software > 280399 Computer Software not elsewhere classified</a><br /><a href="http://eprints.utas.edu.au/view/subjects/280207.html">280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280207 Pattern Recognition</a></td></tr><tr><th valign="top" class="ep_row">ID Code:</th><td valign="top" class="ep_row">933</td></tr><tr><th valign="top" class="ep_row">Deposited By:</th><td valign="top" class="ep_row"><span class="ep_name_citation"><span class="person_name">Mr Samuel J Welch</span></span></td></tr><tr><th valign="top" class="ep_row">Deposited On:</th><td valign="top" class="ep_row">27 Apr 2007</td></tr><tr><th valign="top" class="ep_row">Last Modified:</th><td valign="top" class="ep_row">09 Jan 2008 02:30</td></tr><tr><th valign="top" class="ep_row">ePrint Statistics:</th><td valign="top" class="ep_row"><a target="ePrintStats" href="/es/index.php?action=show_detail_eprint;id=933;">View statistics for this ePrint</a></td></tr></table><p align="right">Repository Staff Only: <a href="http://eprints.utas.edu.au/cgi/users/home?screen=EPrint::View&eprintid=933">item control page</a></p>
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