The capability to observe ocean swell using spaceborne Synthetic Aperture Radar (SAR) has been demonstrated starting with ERS-1 mission in 1992. This dissertation shows how ocean swell properties can be used to combine swell observations of heterogeneous quality and acquired at various times and locations for the observation and forecast of ocean swell fieldsusing ASAR instrument on-board ENVISAT. The first section is a review of how ocean swell spectra can be derived from the SAR complex images of the ocean surface using a quasi-linear transformation. Then, significant swell heights, peak periods and peak directions from in situ measurements are used to assess the accuracy of the SAR observed swell spectra. Using linear propagation in deep ocean, a new swell field reconstruction methodologyis developed in order to gather SAR swell observations related to the same swell field. Propagated from their generation region, these observations render the spatio-temporal properties of the emanating ocean swell fields. Afterwards, a methodology is developed for the exclusion of outliers taking advantage of the swell field consistency. Also, using the irregularly sampled SAR observations, quality controlled estimations of swell field integral parameters are produced on a regular space-time grid. Validation against in situ measurements reveals the dramatic impact of the density of propagated observations on the integral parameters estimated accuracy. Specifically, this parameter is shown to be very dependent on the satellite orbit. Finally, comparisons with the numerical wave model WAVEWATCH-III prove it could potentially benefit from the SAR swell field estimates for assimilation purposes.

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.

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.

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).

This thesis presents a deep learning tool able to identify ice in radar images fromthe sea-ice environment of the Twhaites glacier outlet. The project is motivatedby the threatening situation of the Thwaites glacier that has been increasingits mass loss rate during the last decade. This is of concern considering thelarge mass of ice held by the glacier, that in case of melting, could increasethe mean sea level by more than +65 cm [1]. The algorithm generated alongthis work is intended to help in the generation of navigation charts and identificationof icebergs in future stages of the project, outside of the scope of this thesis.The data used for this task are ICEYE’s X-band radar images from the Thwaitessea-ice environment, the target area to be studied. The corresponding groundtruth for each of the samples has been manually generated identifying the iceand icebergs present in each image. Additional data processing includes tiling,to increment the number of samples, and augmentation, done by horizontal andvertical flips of a random number of tiles.The proposed tool performs semantic segmentation on radar images classifyingthe class "Ice". It is developed by a deep learning Convolutional Neural Network(CNN) model, trained with prepared ICEYE’s radar images. The model reachesvalues of F1 metric higher than 89% in the images of the target area (Thwaitessea-ice environment) and is able to generalize to different regions of Antarctica,reaching values of F1 = 80 %. A potential alternative version of the algorithm isproposed and discussed. This alternative score F1 values higher than F1 > 95 %for images of the target environment and F1 = 87 % for the image of the differentregion. However, it must not be confirmed as the final algorithm due to the needfor further verification.

L’imagerie satellite radar propose un point de vue intéressant pour l’étude et la compréhension des océans. Là où l’altimétrie, reconnue et utilisée mondialement, a su s’imposer comme une source de données majeure, les observations de houle issues du SAR (de l’anglais « Synthetic Aperture Radar ») restent encore largement sous exploitées. L’objet de cette thèse est de promouvoir l’utilisation de ces données en proposant un modèle pour l’analyse et la prévision de la houle à l’échelle du globe qui soit indépendant des modèles numériques classiques comme Wavewatch-III. Ce travail s’inscrit dans une logique de pérennisation de la mesure de houle depuis l’espace avec le lancement dans les trois années à venir des trois missions satellites Sentinel-1 A et B et CFOSAT. Un des principaux résultats de ce travail est la capacité de la méthode développée à fournir une information plus précise que celle des modèles existants. Cette méthode permet également une meilleure caractérisation des mesures utilisées en entrée et des pistes d’amélioration de ces dernières sont dégagées pour les futures activités de calibration/validation. Ces travaux ouvrent également des perspectives sur les possibilités d’assimilation des sorties de ce nouveau modèle dans les modèles numériques classiques. / The capability to observe ocean swell using spaceborne Synthetic Aperture Radar (SAR) has been demonstrated starting with ERS-1 mission in 1992. This dissertation shows how ocean swell properties can be used to combine swell observations of heterogeneous quality and acquired at various times and locations for the observation and forecast of ocean swell fieldsusing ASAR instrument on-board ENVISAT. The first section is a review of how ocean swell spectra can be derived from the SAR complex images of the ocean surface using a quasi-linear transformation. Then, significant swell heights, peak periods and peak directions from in situ measurements are used to assess the accuracy of the SAR observed swell spectra. Using linear propagation in deep ocean, a new swell field reconstruction methodologyis developed in order to gather SAR swell observations related to the same swell field. Propagated from their generation region, these observations render the spatio-temporal properties of the emanating ocean swell fields. Afterwards, a methodology is developed for the exclusion of outliers taking advantage of the swell field consistency. Also, using the irregularly sampled SAR observations, quality controlled estimations of swell field integral parameters are produced on a regular space-time grid. Validation against in situ measurements reveals the dramatic impact of the density of propagated observations on the integral parameters estimated accuracy. Specifically, this parameter is shown to be very dependent on the satellite orbit. Finally, comparisons with the numerical wave model WAVEWATCH-III prove it could potentially benefit from the SAR swell field estimates for assimilation purposes.

This thesis examines the role of remote sensing technology in providing information to northern residents of Kugluktuk and Cambridge Bay, Kitikmeot region of Nunavut, Western Canadian Arctic, for the purpose of improving sea ice trafficability and safety. The main objectives of this thesis include 1) the identification of northern community sea ice information needs that can be addressed using remote sensing, and 2) the creation of remote sensing-based products showing sea ice surface roughness information useful to community sea ice trafficability and safety. Thesis outcomes include the refinement and dissemination of information and products with these communities. Research methods involved interviews with northern community members that were analysed using thematic analysis, as well as quantitative assessments of sea ice roughness using satellite datasets. Maps of sea ice surface roughness were created using Sentinel-1 synthetic aperture radar and the Multi-angle Imaging Spectroradiometer, and were evaluated against fine-scale airborne LiDAR data. / Graduate / 2020-07-31

Los Radares de Apertura Sintética, o sistemas SAR, representan el mejorejemplo de sistemas activos de teledetección por microondas. Debido a su naturaleza coherente, un sistema SAR es capaz de adquirir información dedispersión electromagnética con una alta resolución espacial, pero por otro lado, esta naturaleza coherente provoca también la aparición de speckle.A pesar de que el speckle es una medida electromagnética, sólo puede ser analizada como una componente de ruido debido a la complejidad asociadacon el proceso de dispersión electromagnética.Para eliminar los efectos del ruido speckle adecuadamente, es necesario un modelo de ruido, capaz de identificar las fuentes de ruido y como éstasdegradan la información útil. Mientras que este modelo existe para sistemasSAR unidimensionales, conocido como modelo de ruido speckle multiplicativo,éste no existe en el caso de sistemas SAR multidimensionales.El trabajo presentado en esta tesis presenta la definición y completa validación de nuevos modelos de ruido speckle para sistemas SAR multidimensionales,junto con su aplicación para la reducción de ruido speckle y la extracción de información.En esta tesis, los datos SAR multidimensionales, se consideran bajo una formulación basada en la matriz de covarianza, ya que permite el análisisde datos sobre la base del producto complejo Hermítico de pares de imágenesSAR. Debido a que el mantenimiento de la resolución especial es un aspectoimportante del procesado de imágenes SAR, la reducción de ruido speckleestá basada, en este trabajo, en la teoría de análisis wavelet.

Scene classification is performed on countless remote sensing images in support of operational activities. Automating this process is preferable since manual pixel-level classification is not feasible for large scenes. However, developing such an algorithmic solution is a challenging task due to both scene complexities and sensor limitations. The objective is to develop efficient and accurate unsupervised methods for classification (i.e., assigning each pixel to an appropriate generic class) and for labeling (i.e., properly assigning true labels to each class). Unique from traditional approaches, the proposed bootstrap approach achieves classification and labeling without training data. Here, the full image is partitioned into subimages and the true classes found in each subimage are provided by the user. After these steps, the rest of the process is automatic. Each subimage is individually classified into regions and then using the joint information from all subimages and regions the optimal configuration of labels is found based on an objective function based on a Markov random field (MRF) model. The bootstrap approach has been successfully demonstrated with SAR sea-ice and lake ice images which represent challenging scenes used operationally for ship navigation, climate study, and ice fraction estimation. Accuracy assessment is based on evaluation conducted by third party experts. The bootstrap method is also demonstrated using synthetic and natural images. The impact of this technique is a repeatable and accurate methodology that generates classified maps faster than the standard methodology.

Scene classification is performed on countless remote sensing images in support of operational activities. Automating this process is preferable since manual pixel-level classification is not feasible for large scenes. However, developing such an algorithmic solution is a challenging task due to both scene complexities and sensor limitations. The objective is to develop efficient and accurate unsupervised methods for classification (i.e., assigning each pixel to an appropriate generic class) and for labeling (i.e., properly assigning true labels to each class). Unique from traditional approaches, the proposed bootstrap approach achieves classification and labeling without training data. Here, the full image is partitioned into subimages and the true classes found in each subimage are provided by the user. After these steps, the rest of the process is automatic. Each subimage is individually classified into regions and then using the joint information from all subimages and regions the optimal configuration of labels is found based on an objective function based on a Markov random field (MRF) model. The bootstrap approach has been successfully demonstrated with SAR sea-ice and lake ice images which represent challenging scenes used operationally for ship navigation, climate study, and ice fraction estimation. Accuracy assessment is based on evaluation conducted by third party experts. The bootstrap method is also demonstrated using synthetic and natural images. The impact of this technique is a repeatable and accurate methodology that generates classified maps faster than the standard methodology.