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This thesis focuses on modeling and visualizing dynamic landscape objects and their qualities
. It contains ontologies to characterize and model dynamic landscape features using spatial data. It considers their spatial data qualities and visualizes them by explorative methods. In this study, the dynamic landscape features are derived from a coastal movement application within the Netherlands, whereby beaches are subject to nourishment due to severe erosion. The description and classification of beach objects and their processes essentially grounds on the perception of the coastal landscape. Modeling a landscape is a basic agreement on the conceptualization of these features and processes. The aim is to develop a framework for conceptualization of dynamic beach objects, to understand the physical processes involved and to illustrate decision rules adopted in classification of these objects. Also, quality issues related to beach nourishments are studied, visualized and explored, using new visualization techniques. A domain-specific ontology can serve as a framework for the conceptualization of beach objects and their processes. The discrimination into product and problem ontology supports the guidance for classification of these objects and to elucidate which data ‘fit for use’. Data qualities are assessed using a quality matrix, where ontological features are portrayed against quality elements. Elements of positional, thematic and temporal accuracy and data completeness are considered of high importance for the beach nourishment application. The problem and product ontology helps to define two scenarios; the first determined by the regulations from the Ministry for Public Works; the second grounded on the abilities from an existing spatial dataset. A comparison between them shows that 72.8% of the objects suitable and non-suitable for nourishment are correctly classified. A higher overlap is found in areas where actual beach nourishments were carried out. Inaccuracies in attributes, i.e. altitude, vegetation and wetness, influence the determination of the objects. A sensitivity analysis applied on altitude shows that determinate boundaries for beach nourishment objects are not reasonable and consequently should be treated as vague objects. The ontology for beach objects is extended with a spatio-temporal ontology that considers objects to be vague and dynamic. It contain
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This thesis outlines the development of four image processing algorithms that combine spectral and spatial information for the detection of complex objects on the Earth' surface by remote sensing
. Complex objects are objects that are composed of several smaller parts. These smaller parts may not be spectrally unique in themselves and lack a statistical coherence, thus creating a problem for image processing techniques that operate per pixel. As the human mind is often capable of processing and recognizing such objects by combining spectral and spatial information, a solution to this problem is an image processing technique that is based on the same qualitative reasoning. The thematic focus is on the detection of natural hydrocarbon seepages. Natural hydrocarbon seepages are non-unique in both their spectral and spatial characteristics. The infrared spectrum of oil is easily confused with other bituminous surfaces such as asphalt. The dominant anomaly that results from the presence of hydrocarbons is a circular halo of bare soil. The first algorithm measured the shape of a homogeneous object, based on relations between the area and perimeter of an object and its convex hull. The second algorithm was a template matching algorithm that matched a miniature image to a remotely sensed image. Both algorithms were based on existing techniques and were shown to perform well in combining spectral and spatial information. It is however concluded that both technigues were not enough versatile for the detection of seepage-induced halos. The third and fourth algorithms were specifically designed for the detection of seepage-induced halos. The third algorithm aimed to detect spectrally homogeneous pixels on a circle. The fourth algorithm was an extension of the third, aimed to detect incomplete circles with variable radii and was based on Hough transforms. Results showed that these algorithms can detect halos of bare soil that result from seeping hydrocarbons. It was finally concluded that the knowledge-based spatial-spectral approach is an improvement of over traditional remote sensing image processing methods in the detection of anomalies resulting from natural hydrocarbon seepages. The concept of the presented algorithms in general and the design of these algorithms in particular allow them to be applied to other remote sensing research
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