Thesis (MSc)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: This research considered the automated remote sensing-based classification of wetland extent within the Nuwejaars and Heuningnes River systems on the Agulhas Plain. The classification process was based on meaningful image objects created through image segmentation rather than on single pixels. An expert system classifier was compared to a nearest-neighbour supervised classifier, and one multispectral (SPOT 5) image (dry season) and two C-band, VV-polarisation synthetic aperture radar (SAR: ERS-2) images (dry and wet season) were used separately and in combination. Classifications were performed within two subset areas. Final classes identified were Permanent waterbody, Other wetland and Non-wetland. Statistical accuracy assessment was performed. Validation data was derived from a combination of high-resolution aerial photographs, the SPOT 5 image, high-resolution imagery on Google Earth and observations during a field visit. Wetland extent was defined as the total extent of wetland-specific vegetation, unvegetated seasonal pans and waterbodies. More detailed classes were originally envisaged, but available validation data was not considered adequate for assessing their accuracy with any confidence. The supervised classifier was found to be more accurate overall than the developed expert system. The difference between the two was however not always significant. The two SAR images alone did not contain sufficient information for the accurate classification of Agulhas wetlands’ extent, with recorded overall accuracies not exceeding 65% regardless of the classifier used. The SPOT image alone achieved accuracies higher than 80%; this was considered a good result. In comparison, combining the SAR and SPOT data did not improve the classification accuracy. The potential of the expert system to be applied with little modification to images acquired over other areas or over the same area in other years should be further investigated. However, several reservations are noted in this regard. Future research could potentially improve the results obtained from supervised classification by augmenting it with expert system rules to identify more complicated classes. KEYWORDS ERS-2, SPOT 5, SAR, wetlands, expert system classifier, nearest-neighbour supervised classifier / AFRIKAANSE OPSOMMING: Hierdie navorsing het die geoutomatiseerde afstandswaarneminggebaseerde klassifikasie van vleilandomvang binne die Nuwejaars- en Heuningnesrivier stelsels op die Agulhasvlakte ondersoek. Die klassifikasieproses was gebaseer op betekenisvolle beeldobjekte geskep deur middel van beeldsegmentasie eerder as op enkele beeldelemente. ‘n Deskundige stelsel klassifiseerder is vergelyk met ‘n naaste-naburige gerigte klassifiseerder. Een multispektrale (SPOT 5) beeld vir die droë seisoen, sowel as twee C-band, VV-polarisasie sintetiese diafragma radar (SAR, ERS2) beelde (vir die droë en nat seisoene) is afsonderlik en in kombinasie gebruik. Klassifikasies is uitgevoer binne twee sub-areas in die beelde. Finale klasse wat geïdentifiseer is was Permanente waterliggaam, Ander vleiland en Nie-vleiland. Statistiese akkuraatheidsassessering is uitgevoer. Verwysingsdata is geskep vanuit ‘n kombinasie van hoë- resolusie lugfoto’s, die SPOT 5 beeld, hoë-resolusie beelde op Google Earth en waarnemings tydens ‘n besoek aan die studiegebied. Vleiland omvang is gedefinieer as die totale omvang van vleiland-spesifieke plantegroei, onbegroeide seisoenale panne en waterliggame. Die gerigte klassifiseerder blyk om oor die algemeen meer akkuraat as die ontwikkelde deskundige stelsel te wees. Die verskil was egter nie altyd beduidend nie. Die twee SAR beelde alleen het nie genoegsame inligting bevat vir die akkurate klassifikasie van Agulhas-vleilande se omvang nie, met behaalde algehele akkuraatheidsvlakke wat nie 65% oorskry het nie, ongeag van die klassifiseerder. Die SPOT-beeld alleenlik het algehele akkuraathede van meer as 80% behaal; wat as ‘n goeie resultaat beskou kan word. In vergelyking hiermee kon die kombinering van SAR- en SPOT-data nie ‘n verbetering teweeg bring nie. Die potensiaal van die deskundige stelsel om met min aanpassing op beelde van ander gebiede of van dieselfde gebied in ander jare toegepas te word, verg verdere ondersoek. Verskeie voorbehoude word egter in hierdie verband gemeld. Toekomstige navorsing kan potensieel die resultate van gerigte klassifikasie verbeter deur dit aan te vul met deskundige stelsel reëls vir die klassifikasie van meer komplekse klasse. TREFWOORDE ERS-2, SPOT 5, SAR, vleilande, deskundige stelsel klassifiseerder, naaste-naburige gerigte klassifiseerder.

Dans cette thèse, nous nous intéressons à un récent algorithme de segmentation d’images basé sur un processus de régularisation de graphes. L’objectif d’un tel algorithme est de calculer une fonction indicatrice de la segmentation qui satisfait un critère de régularité ainsi qu’un critère d’attache aux données. La particularité de cette approche est de représenter les images à l’aide de graphes de similarité. Ceux-ci permettent d’établir des relations entre des pixels non-adjacents, et ainsi de procéder à un traitement non-local des images. Afin d’en améliorer la précision, nous combinons cet algorithme à une seconde approche non-locale : des caractéristiques de textures. Un nouveau terme d’attache aux données est dans un premier temps développé. Inspiré des travaux de Chan et Vese, celui-ci permet d’évaluer l’homogénéité d’un ensemble de caractéristiques de textures. Dans un second temps, nous déléguons le calcul de l’attache aux données à un classificateur supervisé. Entrainé à reconnaitre certaines classes de textures, ce classificateur permet d’identifier les caractéristiques les plus pertinentes, et ainsi de fournir une modélisation plus aboutie du problème. Cette seconde approche permet par ailleurs une segmentation multiclasse. Ces deux méthodes ont été appliquées à la segmentation d’images texturées 2D et 3D. / In this thesis, we improve a recent image segmentation algorithm based on a graph regularization process. The goal of this method is to compute an indicator function that satisfies a regularity and a fidelity criteria. Its particularity is to represent images with similarity graphs. This data structure allows relations to be established between similar pixels, leading to non-local processing of the data. In order to improve this approach, combine it with another non-local one: the texture features. Two solutions are developped, both based on Haralick features. In the first one, we propose a new fidelity term which is based on the work of Chan and Vese and is able to evaluate the homogeneity of texture features. In the second method, we propose to replace the fidelity criteria by the output of a supervised classifier. Trained to recognize several textures, the classifier is able to produce a better modelization of the problem by identifying the most relevant texture features. This method is also extended to multiclass segmentation problems. Both are applied to 2D and 3D textured images.