In this study the use of two radar interferometry methods, PSInSAR and SBAS, were tested as tools for measuring coastal erosion. If successful it would have allowed for measuring coastal erosion as a function of material lost. The study area used was Ystad municipality, in southern Sweden. Radar data for the study was provided by the ESA, the European space agency, from their ERS-2 and ENVISAT satellites, spanning the period 1998-2005. Unfortunately, even after many different configurations of settings were tested, the results indicated that both methods are very unsuited for use in rural areas such as Ystad, whether for measuring coastal erosion or otherwise. Both methods had severe problems achieving significant coverage after low coherence areas were masked out, and PSInSAR suffered from several anomalies. This is likely due to the highly vegetated nature of the landscape, which results in low coherence through temporal decorrelation. Of the two methods SBAS showed the most promise, but not nearly enough to be considereduseful. It is, based on the scientific literature, possible that simpler interferometry methods might have been more useful. This, and other possible ways to improve the results is something that this study discusses at length.

Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references. Available also from UMI/Dissertation Abstracts International.

Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 218-228). Available also in a digital version from Dissertation Abstracts.

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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.

Multitemporal SAR images have been used successfully for the detection of different types of environmental changes. The detection of urban change using SAR images is complicated due to the special characteristics of SAR images—for example, the existence of speckle and the complex mixture of the urban environment. This thesis investigates the detection of urban changes using SAR images with the following specific objectives: (1) to investigate unsupervised change detection, (2) to investigate reduction of the speckle effect and (3) to investigate spatio-contextual change detection. Beijing and Shanghai, the largest cities in China, were selected as study areas. Multitemporal SAR images acquired by ERS-2 SAR (1998~1999) and Envisat ASAR (2008~2009) sensors were used to detect changes that have occurred in these cities. Unsupervised change detection using SAR images is investigated using the Kittler-Illingworth algorithm. The problem associated with the diversity of urban changes—namely, more than one typology of change—is addressed using the modified ratio operator. This operator clusters both positive and negative changes on one side of the change-image histogram. To model the statistics of the changed and the unchanged classes, four different probability density functions were tested. The analysis indicates that the quality of the resulting change map will strongly depends on the density model chosen. The analysis also suggests that use of a local adaptive filter (e.g., enhanced Lee) removes fine geometric details from the scene. Speckle suppression and geometric detail preservation in SAR-based change detection, are addressed using the nonlocal means (NLM) algorithm. In this algorithm, denoising is achieved through a weighted averaging process, in which the weights are a function of the similarity of small image patches defined around each pixel in the image. To decrease the computational complexity, the PCA technique is used to reduce the dimensionality of the neighbourhood feature vectors. Simple methods to estimate the dimensionality of the new space and the required noise variance are proposed. The experimental results show that the NLM algorithm outperformed traditional local adaptive filters (e.g., enhanced Lee) in eliminating the effect of speckle and in maintaining the geometric structures in the scene. The analysis also indicates that filtering the change variable instead of the individual SAR images is effective in terms of both the quality of the results and the time needed to carry out the computation. The third research focuses on the application of Markov random field (MRF) in change detection using SAR images. The MRF-based change detection algorithm shows limited capacity to simultaneously maintain fine geometric detail in urban areas and combat the effect of speckle noise. This problem has been addressed through the introduction of a global constraint on the pixels’ class labels. Based on NLM theory, a global probability model is developed. The iterated conditional mode (ICM) scheme for the optimization of the MAP-MRF criterion function is extended to include a step that forces the maximization of the global probability model. The experimental results show that the proposed algorithm is better at preserving the fine structural detail, effective in reducing the effect of speckle, less sensitive to the value of the contextual parameter, and less affected by the quality of the initial change map compared with traditional MRF-based change detection algorithm. / <p>QC 20130610</p>