In the light of the ongoing loss of biodiversity at the global scale, monitoring grasslands is nowadays of utmost importance considering their functional relevance in terms of the ecosystem services that they provide. Here, guidelines of the European Union like the Fauna-Flora-Habitat Directive and the European Agricultural fund for Rural Development with its HNV indicators are crucial. Indeed, they form the legal framework for nature conservation and define grasslands as one of their conservation targets, whose status needs to be assessed and reported by all member states on a regular basis. In the light of these reporting requirements, the need for a harmonised and thorough grassland monitoring is highly demanding since most member states are still currently adopting intensive field surveys or photointerpretation with differing levels of detail for mapping habitat distribution. To this purpose, a cost-effective solution is offered by Earth Observation data for which specific grassland monitoring methodologies shall be then implemented which are capable of processing multitemporal acquisitions collected throughout the entire growing season. Although optical data are most suited for characterising vegetation in terms of spectral information content, they are actually subject to weather conditions (especially cloud coverage), which hinder the possibility of collecting enough information over the full phenological cycle. Furthermore, so far only few studies started employing high and very high resolution optical time series for grassland habitat monitoring since they have become available e.g., from the RapidEye satellites, only in the recent past. To overcome this limitation, SAR systems can be employed which provide imagery independent from weather or daytime conditions, hence enabling vegetation analysis by means of complete time series. Compared to optical data, radar imagery is less affected by the physical-chemical characteristics of the surface, but rather it is sensitive to structural features like geometry and roughness. However, in this context presently only very few techniques have been implemented, which are anyhow not suitable to be employed in an operational framework. Furthermore, to address the classification task, supervised approaches (which require in situ information for all the land-cover classes present in the study area) represent the most accurate methodological solution; nevertheless, collecting an exhaustive ground truth is generally expensive both in terms of time and economic costs and is not even feasible when the test site is remote. However, in many applications the end-users are generally only interested in very few specific targeted land-cover classes which, for instance, have high ecological value or are associated with support actions, subsidies or benefits from national or international institutions. The categorisation of specific grasslands and habitat types as those addressed in this thesis falls within such category of problems, which is defined in the literature as targeted land-cover classification. In this framework, a robust and effective targeted classification system for the automatic identification of grassland types by means of multi-temporal and multi-polarised SAR data has been developed within this thesis. In particular, the proposed system is composed of three main blocks: the preprocessing of the SAR image time series including the Kennaugh decomposition, the feature extraction including multi-temporal filtering and texture analysis, and the hierarchical targeted classification, which consist of two phases where first a one-class classifier is employed to outline the merger of all the grassland types of interest considered as a single information class and then a multi-class classifier is applied for discriminating the specific targeted classes within the areas identified as positive by the one-class classifier. To evaluate the capabilities of the proposed methodology, several experimental trials have been carried out over two test sites located in Southern Bavaria (Germany) and Mecklenburg Western-Pomerania (Germany) for which six diverse datasets have been derived from multitemporal series of dualpol TerraSAR-X as well as dual-/quadpol Radarsat-2 images. Four among the Natura 2000 habitat types of the Fauna-Flora-Habitat Directive as well all High Nature Value grassland types have been considered as targeted classes for this study. Overall, the proposed system proved to be robust and confirmed the effectiveness of employing multitemporal and multi-polarisation VHR SAR data for discriminating habitat types and High Nature Value grassland types, exhibiting high potential for future employment even at larger scales. In particular, it could be demonstrated that the proposed hierarchical targeted classification approach outperforms the available state-of-the-art methods and has a clear advantage with respect to the standard approaches in terms of robustness, reliability and transferability.

Thesis (MScEng (Process Engineering))--University of Stellenbosch, 2011. / ENGLISH ABSTRACT: Hydrocyclones are separation devices that are widely used throughout the chemical engineering and mineral processing industries. Although simple in design, the intricate flow structure of the device complicates control. As an alternative to conventional empirical and theoretical modelling, process state monitoring methods have recently been employed as a means to control hydrocyclones. The purpose of process state monitoring methods is to distinguish between the desired operating state with favourable separation, the transition state, and the troublesome operating state of dense flow separation. In comparison to previously employed monitoring techniques, image analysis of the underflow is regarded as a promising approach. Preliminary studies have indicated that the technique complies with hydrocyclone monitoring requirements: sensitivity, non-invasiveness, sampling times less than one second, robustness and low cost. The primary objective of this study was therefore defined as investigating the feasibility of image analysis of hydrocyclone underflow as a monitoring technique. Data collection entailed the recording of hydrocyclone underflow for different operating states. Six case studies were performed in total: Gold, Ilmenite, Platreef, Merensky 1, Merensky 2 and Merensky 3 (with the case study names indicating the different ore types used). An image analysis technique, consisting of feature extraction through motion detection, as well as various noise reduction methods, was consequently developed and applied to the video data. Classification of the various operating states was attempted by performing modelling by one-class support vector machines (SVM). Results indicated that the developed image analysis technique effectively addresses background noise, random noise and system vibration through image enhancement and a motion threshold. Extremely low contrast differences and foreground noise did, however, prove problematic in Ilmenite and Merensky 1 case studies respectively. For the remaining case studies, it was found that the various operating states were identified with high accuracy through one-class SVM classification. This is particularly true for the identification of the troublesome dense flow separation for which extremely low missing alarm rates were obtained (0 % in most cases). In terms of practicality, the technique proved to be sensitive, non-intrusive and economical. The sampling time of 30 frames per second and estimated processing to video time ratio of 1:1, is furthermore satisfactory. Ultimately, the results indicate that the image analysis of hydrocyclone underflow is a viable monitoring technique. The robustness of the technique might further be improved by use of backlighting and an air-knife. It is also recommended that future work should focus on testing the monitoring technique on an industrial hydrocyclone setup. / AFRIKAANSE OPSOMMING: Hidrosiklone is skeidingsapparate wat algemeen gebruik word in chemiese ingenieurswese en mineraalprosesserings industrieë. Alhoewel die apparaat ‘n eenvoudige ontwerp het, bemoeilik die komplekse interne vloeistruktuur die beheer daarvan. Prosestoestandmoniteringsmetodes is vir hidrosikloonbeheer toegepas as alternatief vir konvensionele empiriese en teoretiese modellering. Die doel van prosestoestandmoniteringsmetodes is om te onderskei tussen die gewenste bedryfstoestand met gunstige skeiding, die oorgangstoestand, en die moeilike bedryfstoestand van digtevloeiskeiding. In vergelyking met vorige toegepaste moniteringstegnieke, word beeldverwerking van die ondervloei beskou as ‘n belowende tegniek. Voorlopige studies het aangedui dat die tegniek voldoen aan die hidrosikloonmoniteringvereistes: sensitiwiteit, nie-indringendheid, monsternemingstydperke laer as een sekonde, robuustheid en lae koste. Die primêre doelwit van hierdie studie is daarom gedefineer as die ondersoek van die doenlikheid van beeldverwerking van hidrosikloon ondervloei as ‘n moniteringstegniek. Die data versameling het die afneem van hidrosikloon ondervloei vir verskillende bedryfstoestande behels. Ses gevallestudies is in totaal uitgevoer: Goud, Ilmeniet, Platreef, Merensky 1, Merensky 2 en Merensky 3 (die gevallestudie name dui die verskillende erts tipes wat gebruik is aan). ‘n Beeldverwerkingstegniek, wat bestaan uit kenmerkekstraksie deur bewegingsopsporing, asook verskeie geruisverlagingsmetodes, is gevolglik ontwikkel en toegepas op die video data. Klassifikasie van die verskeie bedryfstoestande is beproef deur modellering met enkelklassteunvektormasjiene. Resultate het aangedui dat die ontwikkelde beeldverwerkingstegniek agtergrond geruis, onreëlmatige geruis en sisteem vibrasie suksesvol aanspreek deur beeldversterking en ‘n bewegingslimiet. Beduidende lae kontrasverskille en voorgrond geruis blyk wel problematies in die Ilmeniet en Merensky 1 gevallestudies onderskeidelik. Vir die orige gevallestudies is gevind dat die verskillinde bedryfstoestande met hoë akkuraatheid geïdentifiseer is deur enkelklassteunvektormasjiene klassifisering. Dit is veral waar vir die identifisering van die moeilike digtevloeiskeiding waarvoor beduidende lae vermiste-alarmmaatstawwe behaal is (0 % in die meeste gevalle). Aangaande die praktiese aspekte, blyk die tegniek sensitief, nie-indringend en ekonomies. Die monsternemingstydperk van 30 raampies per sekonde en die beraamde prosesserings- tot videotyd verhouding van 1:1, is ook voldoende. Ten slotte dui die resultate daarop dat die beeldverwerking van hidrosikloon ondervloei ‘n uitvoerbare moniteringstegniek is. Die robuustheid van die tegniek sou verder verbeter kon word deur gebruik te maak van agtergrondverligting en ‘n lugspuit. Dit word ook aanbeveel dat toekomstige werk op die toetsing van die moniteringstegniek op ‘n industriële hidrosikloon toestel moet fokus.

Road Networks play an important role in various applications such as urban and rural planning, infrastructure planning, transportation management, vehicle navigation. Extraction of Roads from Remote Sensed satellite images for updating road database in geographical information systems (GIS) is generally done manually by a human operator. However, manual extraction of roads is time consuming and labor intensive process. In the existing literature, there are a great number of researches published for the purpose of automating the road extraction process. However, automated processes still yield some erroneous and incomplete results and human intervention is still required. The aim of this research is to propose a framework for road network extraction from high spatial resolution multi-spectral imagery (MSI) to improve the accuracy of road extraction systems. The proposed framework begins with a spectral classification using One-class Support Vector Machines (SVM) and Gaussian Mixture Models (GMM) classifiers. Spectral Classification exploits the spectral signature of road surfaces to classify road pixels. Then, an iterative template matching filter is proposed to refine spectral classification results. K-medians clustering algorithm is employed to detect candidate road centerline points. Final road network formation is achieved by Markov Random Fields. The extracted road network is evaluated against a reference dataset using a set of quality metrics.