Thesis (MSc)--Stellenbosch University, 2005. / ENGLISH ABSTRACT: Remote sensing and image processing tools provide speedy and up-to-date information on land
resources. Although remote sensing is the most effective means of land cover and land use mapping, it
is not without limitations. The accuracy of image analysis depends on a number of factors, of which the
image classifier used is probably the most significant. It is noted that there is no perfect classifier, but
some robust classifiers achieve higher accuracy results than others. For certain land cover/uses,
discrimination based only on spectral properties is extremely difficult and often produces poor results.
The use of ancillary data can improve the classification process. Some classifiers incorporate ancillary
data before or after the classification process, which limits the full utilization of the information
contained in the ancillary data. Expert classification, on the other hand, makes better use of ancillary
data by incorporating data directly into the classification process.
In this study an expert classification model was developed based on spatial operations designed to
identify a specific land cover/use, by integrating both spectral and available ancillary data. Ancillary
data were derived either from the spectral channels or from other spatial data sources such as DEM
(Digital Elevation Model) and topographical maps. The model was developed in ERDAS Imagine
image-processing software, using the expert engineer as a final integrator of the different constituent
spatial operations. An attempt was made to identify the Level I land cover classes in the South African
National Land Cover classification scheme hierarchy. Rules were determined on the basis of expert
knowledge or statistical calculations of mean and variance on training samples. Although rules could
be determined by using statistical applications, such as the classification analysis regression tree
(CART), the absence of adequate and accurate training data for all land cover classes and the fact that
all land cover classes do not require the same predictor variables makes this option less desirable. The
result of the accuracy assessment showed that the overall classification accuracy was 84.3% and kappa
statistics 0.829. Although this level of accuracy might be suitable for most applications, the model is
flexible enough to be improved further. / AFRIKAANSE OPSOMMING: Afstandswaameming-en beeldverwerkingstegnieke kan akkurate informasie oorbodemhulpbronne
weergee. Alhoewel afstandswaameming die mees effektiewe manier van grondbedekking en
grondgebruikkartering is, is dit nie sonder beperkinge nie. Die akkuraatheid van beeldverwerking is
afhanklik van verskeie faktore, waarvan die beeld klassifiseerder wat gebruik word, waarskynlik die
belangrikste faktor is. Dit is welbekend dat daar geen perfekte klassifiseerder is nie, alhoewel sekere
kragtige klassifiseerders hoër akkuraatheid as ander behaal. Vir sekere grondbedekking en -gebruike is
uitkenning gebaseer op spektrale eienskappe uiters moeilik en dikwels word swak resultate behaal. Die
gebruik van aanvullende data, kan die klassifikasieproses verbeter. Sommige klassifiseerders
inkorporeer aanvullende data voor of na die klassifikasieproses, wat die volle aanwending van die
informasie in die aanvullende data beperk. Deskundige klassifikasie, aan die ander kant, maak beter
gebruik van aanvullende data deurdat dit data direk in die klassifikasieproses inkorporeer.
Tydens hierdie studie is 'n deskundige klassifikasiemodel ontwikkel gebaseer op ruimtelike
verwerkings, wat ontwerp is om spesifieke grondbedekking en -gebruike te identifiseer. Laasgenoemde
is behaal deur beide spektrale en beskikbare aanvullende data te integreer. Aanvullende data is afgelei
van, óf spektrale eienskappe, óf ander ruimtelike bronne soos 'n DEM (Digitale Elevasie Model) en
topografiese kaarte. Die model is ontwikkel in ERDAS Imagine beeldverwerking sagteware, waar die
'expert engineer' as finale integreerder van die verskillende samestellende ruimtelike verwerkings
gebruik is. 'n Poging is aangewend om die Klas I grondbedekkingklasse, in die Suid-Afrikaanse
Nasionale Grondbedekking klassifikasiesisteem te identifiseer. Reëls is vasgestel aan die hand van
deskundige begrippe of eenvoudige statistiese berekeninge van die gemiddelde en variansie van
opleidingsdata. Alhoewel reëls met behulp van statistiese toepassings, soos die 'classification analysis
regression tree (CART)' vasgestel kon word, maak die afwesigheid van genoegsame en akkurate
opleidingsdata vir al die grondbedekkingsklasse hierdie opsie minder aantreklik. Bykomend tot
laasgenoemde, vereis alle grondbedekkingsklasse nie dieselfde voorspellingsveranderlikes nie. Die
resultaat van hierdie akkuraatheidsskatting toon dat die algehele klassifikasie-akkuraatheid 84.3% was
en die kappa statistieke 0.829. Alhoewel hierdie vlak van akkuraatheid vir die meeste toepassings
geskik is, is die model aanpasbaar genoeg om verder te verbeter.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/50445 |
| Date | 04 1900 |
| Creators | Kidane, Dawit K. |
| Contributors | Van Niekerk, A., Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies. |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | Unknown |
| Type | Thesis |
| Format | 86 p. : ill., maps |
| Rights | Stellenbosch University |
Page generated in 0.0022 seconds