Thesis (MSc (Geography and Environmental Studies))--University of Stellenbosch, 2010. / 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. / 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.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/4363 |
| Date | 03 1900 |
| Creators | Stephenson, Garth Roy |
| Contributors | Van Niekerk, Adriaan, University of Stellenbosch. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies. |
| Publisher | Stellenbosch : University of Stellenbosch |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 60 p. : ill., maps |
| Rights | University of Stellenbosch |
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