In remote sensing, segmentation is a procedure of partitioning the domain of a remotely sensed dataset into meaningful regions which correspond to different land use and land cover (LULC) classes or part of them. So far, the remotely sensed data segmentation is still one of the most challenging problems addressed by the remote sensing community, partly because of the availability of remotely sensed data from diverse sensors of various platforms with very high spatial resolution (VHSR). Thus, there is a strong motivation to propose a sophisticated data representation that can capture the significant amount of details presented in a VHSR dataset and to search for a more powerful scheme suitable for multiple remotely sensed data segmentations. This thesis focuses on the development of a segmentation framework for multiple VHSR remotely sensed data. The emphases are on VHSR data model and segmentation strategy. Starting with the domain partition of a given remotely sensed dataset, a hierarchical data model characterizing the structures hidden in the dataset locally, regionally and globally is built by three random fields: Markova random field (MRF), strict stationary random field (RF) and label field. After defining prior probability distributions which should capture and characterize general and scene-specific knowledge about model parameters and the contextual structure of accurate segmentations, the Bayesian based segmentation framework, which can lead to algorithmic implementation for multiple remotely sensed data, is developed by integrating both the data model and the prior knowledge. To verify the applicability and effectiveness of the proposed segmentation framework, the segmentation algorithms for different types of remotely sensed data are designed within the proposed segmentation framework. The first application relates to SAR intensity image processing, including segmentation and dark spot detection by marked point process. In the second application, the algorithms for LiDAR point cloud segmentation and building detection are developed. Finally, texture and colour texture segmentation problems are tackled within the segmentation framework. All applications demonstrate that the proposed data model provides efficient representations for hierarchical structures hidden in remotely sensed data and the developed segmentation framework leads to successful data processing algorithms for multiple data and task such as segmentation and object detection.

The aim of this thesis is to develop a methodology for detection of naturally occurring offshore oil slicks originating from hydrocarbon seeps using satellite remote sensing techniques. In this scope, Synthetic Aperture Radar (SAR) imagery has been utilized. Case study area was Andrusov High in the Central Black Sea. Hydrocarbon seepage from tectonic or stratigraphic origin at the sea floor causes oily gas plumes to rise up to the sea surface. They form thin oil films on the sea surface called oil slicks. Presence of seeps and surface oil slicks for the offshore basins is a trace of depleted oil traps. Spatial distribution of oil slicks is closely related to sea waves, dominant wind patterns and weathering factors. Even though, there are oil slick detection techniques available with optical remote sensing, laser fluorosensors, and hyperspectral remote sensing, the most efficient results can be obtained from active microwave sensors like synthetic aperture radar (SAR). SAR sensors simply measure the backscattered radiation from the surface and show the roughness of the terrain. Oil slicks dampen the sea waves creating dark patches in the SAR image. In this context an adapted methodology has been proposed, including three levels namely / visual inspection, image filtering and object based fuzzy classification. With visual inspection, targets have been identified and subset scenes have been created. Subset scenes have been categorized into 3 cases based on contrast difference of dark spots to the surroundings. Then object based classification has been utilized with the fuzzy membership functions defined by extracted features of layer values, shape and texture from segmented and filtered SAR subsets. As a result, oil slicks have been discriminated from look-alikes which are the phenomena resembling oil slicks. The overall classification accuracy obtained by averaging three different cases is 83 % for oil slicks and 77 % for look-alikes. The results of this study can considered to be a preliminary work and supplementary information for determining the best operational procedure of offshore hydrocarbon exploration.

Soil moisture condition of a watershed plays a significant role in separation of rainfall into infiltration and surface runoff, and hence is a key parameter for the majority of physical hydrological models. Due to the large difference in dielectric constants of dry soil and water, microwave remote sensing and particularly the commonly available synthetic aperture radar is a potential tool for such studies. The main aim of this study is to produce the distributed soil moisture maps of a catchment from active microwave imagery. For this purpose, nine field trips are performed within a small basin in western Anatolia and point surface soil moisture values are collected with a Time Domain Reflectometer. The field studies are planned to match radar image acquisitions and accomplished over the water year of 2004 - 2005. In this context, first, the Dubois Model, a semi-empirical backscatter model is utilized in the reverse order to develop radar backscatter &amp / #8211 / soil roughness relationship and soil roughness maps of the study area are obtained. Then another relationship is built between radar backscatter and the three governing surface parameters: local incidence angle, soil moisture and soil roughness, which is later used in the soil moisture estimation methods. Depending on land use and vegetation cover condition, surface soil moisture maps of the catchment are produced by Backscatter Correction Factors, Water Cloud Model and Basin Indexes methods. In the last part of the study, the soil moisture maps of the basin are input to a semi-distributed hydrological model, HEC-HMS, as the initial soil moisture condition of a flood event simulation. In order to investigate the contribution of distributed initial soil moisture data on model outputs, simulation of the same flood event is also performed with the lumped initial soil moisture condition. Finally, a comparison between both the distributed and lumped model simulation outputs and with the observed data is carried out.

In the scope of this thesis, simulation-based analyses and comparative evaluation of Synthetic Aperture Radar (SAR) image formation techniques, namely Time Domain Correlation, Range Stacking, Range Doppler and Chirp Scaling algorithms, are presented. For this purpose, first, the fundamental concepts of SAR such as SAR geometry, resolution and signal properties are explained. A broadside SAR simulator that provides artificial raw data as an input to the algorithms is designed and implemented. Then, the mathematical background of the imaging algorithms discussed in the thesis is provided. Implementations of these algorithms and simulations are carried out using MATLAB&reg / . Finally, simulation results are presented and discussed to show the advantages and disadvantages of the algorithms.

Before launching into ultrasound research, it is important to recall that the ultimate goal is to provide the clinician with the best possible information needed to make an accurate diagnosis. Ultrasound images are inherently affected by speckle noise, which is due to image formation under coherent waves. Thus, it appears to be sensible to reduce speckle artifacts before performing image analysis, provided that image texture that might distinguish one tissue from another is preserved. The main goal of this thesis was the development of novel speckle suppression methods from medical ultrasound images in the multiscale wavelet domain. We started by showing, through extensive modeling, that the subband decompositions of ultrasound images have significantly non-Gaussian statistics that are best described by families of heavy-tailed distributions such as the alpha-stable. Then, we developed Bayesian estimators that exploit these statistics. We used the alpha-stable model to design both the minimum absolute error (MAE) and the maximum a posteriori (MAP) estimators for alpha-stable signal mixed in Gaussian noise. The resulting noise-removal processors perform non-linear operations on the data and we relate this non-linearity to the degree of non-Gaussianity of the data. We compared our techniques to classical speckle filters and current state-of-the-art soft and hard thresholding methods applied on actual ultrasound medical images and we quantified the achieved performance improvement. Finally, we have shown that our proposed processors can find application in other areas of interest as well, and we have chosen as an illustrative example the case of synthetic aperture radar (SAR) images. / Ο απώτερος σκοπός της έρευνας που παρουσιάζεται σε αυτή τη διδακτορική διατριβή είναι η διάθεση στην κοινότητα των κλινικών επιστημόνων μεθόδων οι οποίες να παρέχουν την καλύτερη δυνατή πληροφορία για να γίνει μια σωστή ιατρική διάγνωση. Οι εικόνες υπερήχων προσβάλλονται ενδογενώς από θόρυβο, ο οποίος οφείλεται στην διαδικασία δημιουργίας των εικόνων μέσω ακτινοβολίας που χρησιμοποιεί σύμφωνες κυματομορφές. Είναι σημαντικό πριν τη διαδικασία ανάλυσης της εικόνας να γίνεται απάλειψη του θορύβου με κατάλληλο τρόπο ώστε να διατηρείται η υφή της εικόνας, η οποία βοηθά στην διάκριση ενός ιστού από έναν άλλο. Κύριος στόχος της διατριβής αυτής υπήρξε η ανάπτυξη νέων μεθόδων καταστολής του θορύβου σε ιατρικές εικόνες υπερήχων στο πεδίο του μετασχηματισμού κυματιδίων. Αρχικά αποδείξαμε μέσω εκτενών πειραμάτων μοντελοποίησης, ότι τα δεδομένα που προκύπτουν από τον διαχωρισμό των εικόνων υπερήχων σε υποπεριοχές συχνοτήτων περιγράφονται επακριβώς από μη-γκαουσιανές κατανομές βαρέων ουρών, όπως είναι οι άλφα-ευσταθείς κατανομές. Κατόπιν, αναπτύξαμε Μπεϋζιανούς εκτιμητές που αξιοποιούν αυτή τη στατιστική περιγραφή. Πιο συγκεκριμένα, χρησιμοποιήσαμε το άλφα-ευσταθές μοντέλο για να σχεδιάσουμε εκτιμητές ελάχιστου απόλυτου λάθος και μέγιστης εκ των υστέρων πιθανότητας για άλφα-ευσταθή σήματα αναμεμειγμένα με μη-γκαουσιανό θόρυβο. Οι επεξεργαστές αφαίρεσης θορύβου που προέκυψαν επενεργούν κατά μη-γραμμικό τρόπο στα δεδομένα και συσχετίζουν με βέλτιστο τρόπο αυτή την μη-γραμμικότητα με τον βαθμό κατά τον οποίο τα δεδομένα είναι μη-γκαουσιανά. Συγκρίναμε τις τεχνικές μας με κλασσικά φίλτρα καθώς και σύγχρονες μεθόδους αυστηρού και μαλακού κατωφλίου εφαρμόζοντάς τες σε πραγματικές ιατρικές εικόνες υπερήχων και ποσοτικοποιήσαμε την απόδοση που επιτεύχθηκε. Τέλος, δείξαμε ότι οι προτεινόμενοι επεξεργαστές μπορούν να βρουν εφαρμογές και σε άλλες περιοχές ενδιαφέροντος και επιλέξαμε ως ενδεικτικό παράδειγμα την περίπτωση εικόνων ραντάρ συνθετικής διατομής.

This dissertation presents new methods for detecting dynamic and thermodynamic characteristics of Arctic sea ice using radar remote sensing. A new technique for sea ice motion detection from sequential satellite synthetic aperture radar (SAR) images was developed and thoroughly validated. The accuracy of the system is 0.43 km obtained from a comparison between SAR-derived ice motion vectors and in-situ sea ice beacon trajectories. For the first time, we evaluated ice motion tracking results derived from co-polarization (HH) and cross-polarization (HV) channels of RADARSAT-2 ScanSAR imagery and formulated a condition where the HV channel is more reliable than the HH channel for ice motion tracking. Sea ice motion is substantially controlled by surface winds. Two new models for ocean surface wind speed retrieval from C-band SAR data have been developed and validated based on a large body of statistics on buoy observations collocated and coincided with RADARSAT-1 and -2 ScanSAR images. The proposed models without wind direction input demonstrated a better accuracy than conventionally used algorithms. As a combination of the developed methods we designed a wind speed-ice motion product which can be a useful tool for studying sea ice dynamics processes in the marginal ice zone. To effectively asses the thermodynamic properties of sea ice advanced tools for modeling electromagnetic (EM) wave scattering from rough natural surfaces are required. In this dissertation we present a new analytical formulation for EM wave scattering from rough boundaries interfacing inhomogeneous media based on the first-order approximation of the small perturbation method. Available solutions in the literature represent special cases of our general solution. The developed scattering theory was applied to experimental data collected at three stations (with different snow thicknesses) in the Beaufort Sea from the research icebreaker Amundsen during the Circumpolar Flaw Lead system study. Good agreement between the model and experimental data were observed for all three case studies. Both model and experimental radar backscatter coefficients were considerably higher for thin snow cover (4 cm) compared to the thick snow cover case (16 cm). Our findings suggest that, winter snow thickness retrieval may be possible from radar observations under particular scattering conditions.

Pendant ces dix dernières années, les lancements successifs de satellites pour l´observation de la Terre dotés de capteurs SAR (Synthetic Aperture Radar) ont permis de montrer son fort potentiel, car ces systèmes sont capables de couvrir de vastes régions avec une résolution élevée, ce qui représente un avantage pour le suivi terrain. Cette thèse propose d’élargir les applications conventionnelles, en s’occupant d´investiguer le potentiel et les limites de l’interférométrie SAR pour la mesure de la déformation du terrain dans la région amazonienne, qui n’a pas encore été étudiée dans ce cadre spécifique. L´application consiste à estimer le déplacement de la surface de la Terre sur la zone urbaine de Manaus, la plus grande ville de l'État d'Amazonas au Brésil, qui représente un site important pour l'exploration pétrolière et gazière et pour le transport. Ce site est entouré par des écosystèmes fragiles, qui le rendent très sensible à la présence de l'industrie pétrolière. Dans ce contexte, une compréhension de la dynamique temporelle et de la distribution spatiale des phénomènes de néotectonique est fondamentale pour la définition de bonnes pratiques de gestion de l'environnement. Au niveau méthodologique, afin de lever les principales difficultés rencontrées pour l’application de l’interférométrie différentielle sur une pile de données Radarsat-1, une stratégie multi-échelles et “model free”, basée sur l´information de déformation non-linéaire au cours du temps est adoptée avec succès. La caractéristique essentielle de cette procédure est la séparation du signal de phase en différentes échelles spatiales pour simplifier la séparation des trois composantes de phase (topographie, atmosphère et déplacement). Cela conduit à une plus grande robustesse et permet l'inversion de composantes de phase pour les petites piles d’images. Au niveau géophysique, l’application de l’interférométrie à l’étude du déplacement du terrain est réalisée pour la première fois sur le milieu de l´Amazonie, en complétant les études de géologie structurale antérieures basées sur les mesures issues de la corrélation des images optiques et les mesures de terrain. En complément des connaissances antérieures, la présente étude apporte une information précise sur l'hypothèse de mouvements de la croûte récents liés aux activités néotectoniques du bassin de l'Amazonie. Les résultats indiquent une zone de mouvement de la croûte adjacente à une structure de drainage circulaire dans la ville de Manaus. Les images Radarsat-1 et 2 acquises sur cette région apportent une meilleure compréhension des activités géologiques et du mouvement de la croûte dans le bassin de l'Amazonie. / During the past decade, successive satellites launches for Earth observation with SAR (Synthetic Aperture Radar) sensors onboard have shown its potential, once these systems are able to cover large areas with high resolution, which represents an advantage for surface monitoring. The mass of data generated has enabled the development of radar interferometry and its relevance to the study of small deformations in urban areas or in fault zones, showing that the technique was able to focus on different spatial scales of deformation, as well as temporal scales ranging from a few weeks to more than a decade. Usually, these types of applications have been limited to non-equatorial regions of the world due to the presence of atmospheric disturbances that affect the radar signal. Given the large size and the remote location of tropical basins such as the Amazon, satellite observations remain as a viable approach to validate existing geophysical models. In this context, this thesis proposes to extend conventional applications, taking care to investigate the potential and limitations of SAR interferometry for the measurement of ground deformation in the Amazon region, not yet studied in this specific context. The research aim is to estimate the displacement at the surface of the Earth on the urban area of Manaus, the largest city in the state of Amazonas in Brazil, which is an important site for oil and gas exploration and the transport. This site is surrounded by fragile ecosystems, which make it very sensitive to the presence of the oil industry. Considering this, an understanding of the temporal dynamics and spatial distribution of neotectonic phenomena is fundamental to the development of best practice environmental management. At the methodological level, to overcome the major challenges of the application of differential interferometry on the data stack Radarsat-1, a multi-scale and "model free" strategy, based on the information of a non-linear deformation over time is passed successfully. The essential feature of this process is the separation of the phase signal into small and large scale spatial contributions to simplify the separation of the three phase components (topography, atmosphere and movement). This leads to a more robust processing and allows the phase component inversion for small piles of images. In the geophysical level, the application of interferometry to investigate the ground movement is performed for the first time in the middle of the Amazon, complementing previous studies of structural geology based on measurements from the correlation of optical images and field measurements. In addition to prior knowledge, this study provides accurate information on the hypothesis of recent crustal movements associated with neotectonic activities of the Amazon basin. The results indicate a range of motion of the adjacent crust structure circular drainage in the city of Manaus. The Radarsat-1 and 2 acquired in this region provide a better understanding of geological activity and crustal movement in the Amazon basin.

Arctic sea ice is rapidly declining in extent, thickness, volume and age, with the majority of the decline in extent observed at the end of the melt season. Advanced melt is a thermodynamic regime and is characterized by the formation of melt ponds on the sea ice surface, which have a lower surface albedo (0.2-0.4) than the surrounding ice (0.5-0.7) allowing more shortwave radiation to enter the system. The loss of multiyear ice (MYI) may have a profound impact on the energy balance of the system because melt ponds on first-year ice (FYI) comprise up to 70% of the ice surface during advanced melt, compared to 40% on MYI. Despite the importance of advanced melt to the ocean-sea ice-atmosphere system, advanced melt and the extent to which winter conditions influence it remain poorly understood due to the highly dynamic nature of melt pond formation and evolution, and a lack of reliable observations during this time. In order to establish quantitative links between winter and subsequent advanced melt conditions, and assess the effects of scale and choice of aggregation features on the relationships, three data aggregation approaches at varied spatial scales were used to compare high resolution satellite GeoEye-1 optical images of melt pond covered sea ice to winter airborne laser scanner surface roughness and electromagnetic induction sea ice thickness measurements. The findings indicate that winter sea ice thickness has a strong association with melt pond fraction (fp) for FYI and MYI. FYI winter surface roughness is correlated with fp, whereas for MYI no association with fp was found. Satellite-borne synthetic aperture radar (SAR) data are heavily relied upon for sea ice observation; however, during advanced melt the reliability of observations is reduced. In preparation for the upcoming launch of the RADARSAT Constellation Mission (RCM), the Kolmogorov-Smirnov (KS) statistical test was used to assess the ability of simulated RCM parameters and grey level co-occurrence matrix (GLCM) derived texture features to discriminate between major ice types during winter and advanced melt, with a focus on advanced melt. RCM parameters with highest discrimination ability in conjunction with optimal GLCM texture features were used as input parameters for Support Vector Machine (SVM) supervised classifications. The results indicate that steep incidence angle RCM parameters show promise for distinguishing between FYI and MYI during advanced melt with an overall classification accuracy of 77.06%. The addition of GLCM texture parameters improved accuracy to 85.91%. This thesis provides valuable contributions to the growing body of literature on fp parameterization and SAR ice type discrimination during advanced melt. / Graduate / 2019-03-21

In this paper, a new millimeter wave 3D imaging radar is proposed. The user just needs to move the radar along a circular track, and high resolution 3D imaging can be generated. The proposed radar uses the movement of itself to synthesize a large aperture in both the azimuth and elevation directions. It can utilize inverse Radon transform to resolve 3D imaging. To improve the sensing result, the compressed sensing approach is further investigated. The simulation and experimental result further illustrated the design. Because a single transceiver circuit is needed, a light, affordable and high resolution 3D mmWave imaging radar is illustrated in the paper.

Volcanic subsidence, caused by partial emptying of magma in the subsurface reservoir has long been observed by spaceborne radar interferometry. Monitoring long-term crustal deformation at the most notable type of volcanic subsidence, caldera, gives us insights of the spatial and hazard-related information of subsurface reservoir. Several subsiding calderas, such as volcanoes on the Galapagos islands have shown a complex ground deformation pattern, which is often composed of a broad deflation signal affecting the entire edifice and a localized subsidence signal focused within the caldera floor. Although numerical or analytical models with multiple reservoirs are proposed as the interpretation, geologically and geophysically evidenced ring structures in the subsurface are often ignored. Therefore, it is still debatable how deep mechanisms relate to the observed deformation patterns near the surface. We aim to understand what kind of activities can lead to the complex deformation. Using two complementary approaches, we study the three-dimensional geometry and kinematics of deflation processes evolving from initial subsidence to later collapse of calderas. Firstly, the analog experiments analyzed by structure-from-motion photogrammetry (SfM) and particle image velocimetry (PIV) helps us to relate the surface deformation to the in-depth structures. Secondly, the numerical modeling using boundary element method (BEM) simulates the characteristic deformation patterns caused by a sill-like source and a ring-fault. Our results show that the volcano-wide broad deflation is primarily caused by the emptying of the deep magma reservoir, whereas the localized deformation on the caldera floor is related to ring-faulting at a shallower depth. The architecture of the ring-fault to a large extent determines the deformation localization on the surface. Since series evidence for ring-faulting at several volcanoes are provided, we highlight that it is vital to include ring-fault activity in numerical or analytical deformation source formulation. Ignoring the process of ring-faulting in models by using multiple point sources for various magma reservoirs will result in erroneous, thus meaningless estimates of depth and volume change of the magmatic reservoir(s).