A comparison of supervised and rule-based object-orientated classification for forest mapping

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dc.contributor.advisor Van Niekerk, Adriaan
dc.contributor.author Stephenson, Garth Roy en_ZA
dc.contributor.other University of Stellenbosch. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies.
dc.date.accessioned 2010-02-24T07:41:14Z en_ZA
dc.date.accessioned 2010-08-13T15:02:02Z
dc.date.available 2010-02-24T07:41:14Z en_ZA
dc.date.available 2010-08-13T15:02:02Z
dc.date.issued 2010-03
dc.identifier.uri http://hdl.handle.net/10019.1/4363
dc.description Thesis (MSc (Geography and Environmental Studies))--University of Stellenbosch, 2010.
dc.description.abstract ENGLISH ABSTRACT: Supervised classifiers are the most popular approach for image classification due to their high accuracies, ease of use and strong theoretical grounding. Their primary disadvantage is the high level of user input required during the creation of the data needed to train the classifier. One alternative to supervised classification is an expert-system rule-based approach where expert knowledge is used to create a set of rules which can be applied to multiple images. This research compared supervised and expert-system rule-based approaches for forest mapping. For this purpose two SPOT 5 images were acquired and atmospherically corrected. Field visits, aerial photography, high resolution imagery and expert forestry knowledge were used for the compilation of the training data and the development of a rule-set. Both approaches were evaluated in an object-orientated environment. It was found that the accuracy of the resulting maps was equivalent, with both techniques returning an overall classification accuracy of 90%. This suggests that cost-effectiveness is the decisive factor for determining which method is superior. Although the development of the rule-set was time-consuming and challenging, it did not require any training data. In contrast, the supervised approach required a large number of training areas for each image classified, which was time-consuming and costly. Significantly more training areas will be required when the technique is applied to large areas, especially when multiple images are used. It was concluded that the rule-set is more cost-effective when applied at regional scale, but it is not viable for mapping small areas. en
dc.description.abstract AFRIKAANSE OPSOMMING: Gerigte klassifiseerders is die gewildste benadering tot beeldklassifikasie as gevolg van hulle hoë graad van akkuraatheid, maklike aanwending en kragtige teoretiese fundering. Die primere nadeel van gerigte klassifikasie is die hoë vlak van gebruikersinsette wat benodig word tydens die skepping van opleidingsdata. 'n Alternatief vir gerigte klassifikasie is 'n deskundige stelsel waarin ‘n reëlgebaseerde benadering gevolg word om deskundige kennis aan te wend vir die opstel van 'n stel reëls wat op meervoudige beelde toegepas kan word. Hierdie navorsing het gerigte en deskundige stelsel benaderings toegepas vir bosboukartering om die twee benaderings met mekaar te vergelyk. Vir dié doel is twee SPOT 5 beelde verkry en atmosferies gekorrigeer. Veldbesoeke, lugfotografie, hoë-resolusie beelde en deskundige bosboukennis is aangewend om opleidingsdata saam te stel en die stel reëls te ontwikkel. Beide benaderings is in 'n objekgeoriënteerde omgewing beoordeel. Die akkuraatheidsvlakke van die resulterende kaarte was ewe hoog vir beide tegnieke met 'n algehele klassifikasie-akkuraatheid van 90%. Dit wil dus voorkom asof koste-effektiwiteit eerder as akkuraatheid die deurslaggewende faktor is om te bepaal watter metode die beste is. Alhoewel die ontwikkeling van die stel reëls tydrowend en uitdagend was, het dit geen opleidingsdata vereis nie. In teenstelling hiermee is 'n groot aantal opleidingsgebiede geskep vir elke beeld wat met gerigte klassifikasie verwerk is – 'n tydrowende en duur opsie. Dit is duidelik dat meer opleidingsgebiede benodig sal word wanneer die tegniek op groot gebiede toegepas word, veral omdat meervoudige beelde gebruik sal word. Gevolglik sal die stel reëls meer kosteeffektief wees wanneer dit op streekskaal toegepas word. ‘n Deskundige stelsel benadering is egter nie lewensvatbaar vir die kartering van klein gebiede nie. af
dc.format.extent 60 p. : ill., maps
dc.language.iso en
dc.publisher Stellenbosch : University of Stellenbosch
dc.subject Remote sensing en_ZA
dc.subject Supervised rule-set classification en_ZA
dc.subject Forest mapping en_ZA
dc.subject Dissertations -- Earth sciences en
dc.subject Theses -- Earth sciences en
dc.title A comparison of supervised and rule-based object-orientated classification for forest mapping en_ZA
dc.type Thesis
dc.rights.holder University of Stellenbosch


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