Semi-automated segment generation for geographic novelty detection using edge and area metrics

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dc.contributor.authorFourie, Christoffen_ZA
dc.contributor.authorVan Niekerk, Adriaanen_ZA
dc.contributor.authorMucina, Ladislaven_ZA
dc.date.accessioned2013-07-03T08:18:55Z
dc.date.available2013-07-03T08:18:55Z
dc.date.issued2012
dc.descriptionCITATION: Fourie, C., Van Niekerk, A. & Mucina, L. 2012. Semi-automated segment generation for geographic novelty detection using edge and area metrics. South African Journal of Geomatics, 1(2):133-148.
dc.descriptionThe original publication is available at http://www.sajg.org.za
dc.description.abstractAn approach to generating accurate image segments for land-cover mapping applications is to model the process as an optimisation problem. Area-based empirical discrepancy metrics are used to evaluate instances of generated segments in the search process. An edge metric, called the pixel correspondence metric (PCM), is evaluated in this approach as a fitness function for segmentation algorithm free-parameter tuning. The edge metric is able to converge to user-provided reference segments in an earth observation mapping problem when adequate training data are available. Two common metaheuristic search functions were tested, namely particle swarm optimisation (PSO) and differential evolution (DE). The edge metric is compared with an area-based metric, regarding classification results of the land-cover elements of interests for an arbitrary problem. The results show the potential of using edge metrics, as opposed to area metrics, for evaluating segments in an optimisation-based segmentation algorithm parameter-tuning approach.en_ZA
dc.description.urihttp://www.sajg.org.za/index.php/sajg/article/view/33
dc.description.versionPublisher's version
dc.format.extent16 pages
dc.identifier.citationFourie, C., Van Niekerk, A. & Mucina, L. 2012. Semi-automated segment generation for geographic novelty detection using edge and area metrics. South African Journal of Geomatics, 1(2):133-148.
dc.identifier.issn2225-8531 (online)
dc.identifier.urihttp://hdl.handle.net/10019.1/81530
dc.languageen
dc.publisherCONSAS Conference
dc.rights.holderAuthors retain copyright
dc.subjectImage processingen_ZA
dc.subjectImage segmentationen_ZA
dc.subjectEdge metricsen_ZA
dc.subjectArea metricsen_ZA
dc.subjectLand cover -- Mapsen_ZA
dc.subjectOptimization (Mathematics)en_ZA
dc.titleSemi-automated segment generation for geographic novelty detection using edge and area metricsen_ZA
dc.typeArticle
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