Journal article Open Access

# Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation

Espindola, Giovana; Camara, Gilberto; Reis, Ilka; Bins, Leonardo; Monteiro, Miguel

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<dc:creator>Espindola, Giovana</dc:creator>
<dc:creator>Camara, Gilberto</dc:creator>
<dc:creator>Reis, Ilka</dc:creator>
<dc:creator>Bins, Leonardo</dc:creator>
<dc:creator>Monteiro, Miguel</dc:creator>
<dc:date>2006-02-06</dc:date>
<dc:description>Region‐growing segmentation algorithms are useful for remote sensing image segmentation. These algorithms need the user to supply control parameters, which control the quality of the resulting segmentation. An objective function is proposed for selecting suitable parameters for region‐growing algorithms to ensure best quality results. It considers that a segmentation has two desirable properties: each of the resulting segments should be internally homogeneous and should be distinguishable from its neighbourhood. The measure combines a spatial autocorrelation indicator that detects separability between regions and a variance indicator that expresses the overall homogeneity of the regions.eng</dc:description>
<dc:identifier>https://zenodo.org/record/3831050</dc:identifier>
<dc:identifier>10.1080/01431160600617194</dc:identifier>
<dc:identifier>oai:zenodo.org:3831050</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:source>International Journal of Remote Sensing 27(14) 3035–3040</dc:source>
<dc:subject>Image Segmentation, Spatial Autocorrelation</dc:subject>
<dc:title>Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation</dc:title>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>publication-article</dc:type>
</oai_dc:dc>

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