On Solving SAR Imaging Inverse Problems Using Non-Convex Regularization with a Cauchy-based Penalty

Synthetic aperture radar (SAR) imagery can provide useful information in a multitude of applications, including climate change, environmental monitoring, meteorology, high dimensional mapping, ship monitoring, or planetary exploration. In this paper, we investigate solutions to a number of inverse problems encountered in SAR imaging. We propose a convex proximal splitting method for the optimization of a cost function that includes a non-convex Cauchy-based penalty. The convergence of the overall cost function optimization is ensured through careful selection of model parameters within a forward-backward (FB) algorithm. The performance of the proposed penalty function is evaluated by solving three standard SAR imaging inverse problems, including super-resolution, image formation, and despeckling, as well as ship wake detection for maritime applications. The proposed method is compared to several methods employing classical penalty functions such as total variation ($TV$) and $L_1$ norms, and to the generalized minimax-concave (GMC) penalty. We show that the proposed Cauchy-based penalty function leads to better image reconstruction results when compared to the reference penalty functions for all SAR imaging inverse problems in this paper.Comment: 18 pages, 7 figure

Quantitative Estimation of Surface Soil Moisture in Agricultural Landscapes using Spaceborne Synthetic Aperture Radar Imaging at Different Frequencies and Polarizations

Soil moisture and its distribution in space and time plays an important role in the surface energy balance at the soil-atmosphere interface. It is a key variable influencing the partitioning of solar energy into latent and sensible heat flux as well as the partitioning of precipitation into runoff and percolation. Due to their large spatial variability, estimation of spatial patterns of soil moisture from field measurements is difficult and not feasible for large scale analyses. In the past decades, Synthetic Aperture Radar (SAR) remote sensing has proven its potential to quantitatively estimate near surface soil moisture at high spatial resolutions. Since the knowledge of the basic SAR concepts is important to understand the impact of different natural terrain features on the quantitative estimation of soil moisture and other surface parameters, the fundamental principles of synthetic aperture radar imaging are discussed. Also the two spaceborne SAR missions whose data was used in this study, the ENVISAT of the European Space Agency (ESA) and the ALOS of the Japanese Aerospace Exploration Agency (JAXA), are introduced. Subsequently, the two essential surface properties in the field of radar remote sensing, surface soil moisture and surface roughness are defined, and the established methods of their measurement are described. The in situ data used in this study, as well as the research area, the River Rur catchment, with the individual test sites where the data was collected between 2007 and 2010, are specified. On this basis, the important scattering theories in radar polarimetry are discussed and their application is demonstrated using novel polarimetric ALOS/PALSAR data. A critical review of different classical approaches to invert soil moisture from SAR imaging is provided. Five prevalent models have been chosen with the aim to provide an overview of the evolution of ideas and techniques in the field of soil moisture estimation from active microwave data. As the core of this work, a new semi-empirical model for the inversion of surface soil moisture from dual polarimetric L-band SAR data is introduced. This novel approach utilizes advanced polarimetric decomposition techniques to correct for the disturbing effects from surface roughness and vegetation on the soil moisture retrieval without the use of a priori knowledge. The land use specific algorithms for bare soil, grassland, sugar beet, and winter wheat allow quantitative estimations with accuracies in the order of 4 Vol.-%. Application of remotely sensed soil moisture patterns is demonstrated on the basis of mesoscale SAR data by investigating the variability of soil moisture patterns at different spatial scales ranging from field scale to catchment scale. The results show that the variability of surface soil moisture decreases with increasing wetness states at all scales. Finally, the conclusions from this dissertational research are summarized and future perspectives on how to extend the proposed model by means of improved ground based measurements and upcoming advances in sensor technology are discussed. The results obtained in this thesis lead to the conclusion that state-of-the-art spaceborne dual polarimetric L-band SAR systems are not only suitable to accurately retrieve surface soil moisture contents of bare as well as of vegetated agricultural fields and grassland, but for the first time also allow investigating within-field spatial heterogeneities from space

Scalable computing for earth observation - Application on Sea Ice analysis

In recent years, Deep learning (DL) networks have shown considerable improvements and have become a preferred methodology in many different applications. These networks have outperformed other classical techniques, particularly in large data settings. In earth observation from the satellite field, for example, DL algorithms have demonstrated the ability to learn complicated nonlinear relationships in input data accurately. Thus, it contributed to advancement in this field. However, the training process of these networks has heavy computational overheads. The reason is two-fold: The sizable complexity of these networks and the high number of training samples needed to learn all parameters comprising these architectures. Although the quantity of training data enhances the accuracy of the trained models in general, the computational cost may restrict the amount of analysis that can be done. This issue is particularly critical in satellite remote sensing, where a myriad of satellites generate an enormous amount of data daily, and acquiring in-situ ground truth for building a large training dataset is a fundamental prerequisite. This dissertation considers various aspects of deep learning based sea ice monitoring from SAR data. In this application, labeling data is very costly and time-consuming. Also, in some cases, it is not even achievable due to challenges in establishing the required domain knowledge, specifically when it comes to monitoring Arctic Sea ice with Synthetic Aperture Radar (SAR), which is the application domain of this thesis. Because the Arctic is remote, has long dark seasons, and has a very dynamic weather system, the collection of reliable in-situ data is very demanding. In addition to the challenges of interpreting SAR data of sea ice, this issue makes SAR-based sea ice analysis with DL networks a complicated process. We propose novel DL methods to cope with the problems of scarce training data and address the computational cost of the training process. We analyze DL network capabilities based on self-designed architectures and learn strategies, such as transfer learning for sea ice classification. We also address the scarcity of training data by proposing a novel deep semi-supervised learning method based on SAR data for incorporating unlabeled data information into the training process. Finally, a new distributed DL method that can be used in a semi-supervised manner is proposed to address the computational complexity of deep neural network training

Compensated Row-Column Ultrasound Imaging System

Ultrasound imaging is a valuable tool in many applications ranging from material science to medical imaging. While 2-D ultrasound imaging is more commonly used, 3-D ultrasound imaging offers unique opportunities that can only be found with the help of the extra dimension. Acquiring a 3-D ultrasound image can be done in two main ways: mechanically moving a transducer over a region of interest and using a fixed 2-D transducer. Mechanical motion introduces unwanted artifacts and increases image acquisition time, so a fixed 2-D is usually preferred. However, a fully addressed 2-D array will require a significant amount of connections and data to handle. This motivated the exploration of different simplification schemes to make 2-D arrays for 3-D ultrasound imaging feasible. A method that received a lot of attention for making real-time volumetric ultrasound imaging possible is the row-column method. The row-column method simplifies the fully addressed 2-D array by utilizing a set of 1-D arrays arranged in rows and another set in columns, one set will be responsible for transmit beamforming, while the other for receive beamforming. Using this setup, only $N+N$ connections are needed instead of $N\times N$. This simplification comes at the cost of image quality. Recent advances in row-column ultrasound imaging systems were largely focused on transducer design. However, these imaging systems face a few intrinsic challenges which cannot be addressed through transducer design alone: the issues of sparsity, speckle noise inherent to ultrasound, the spatially varying point spread function, and the ghosting artifacts inherent to the row-column method must all be taken into account. As such, strategies for tackling these intrinsic challenges in row-column imaging would be highly desired to improve imaging quality. In this thesis, we propose a novel compensated row-column ultrasound imaging system where the intrinsic characteristics of the transducer and other aspects of the physical row-column imaging apparatus are leveraged to computationally produce high quality ultrasound imagery. More specifically, the proposed system incorporates a novel conditional random field-driven computational image reconstruction component consisting of two phases: i) characterization and ii) compensation. In the characterization phase, a joint statistical image formation and noise model is introduced for characterizing the intrinsic properties of the physical row-column ultrasound imaging system. In the compensation phase, the developed joint image formation and noise model is incorporated alongside a conditional random field model within an energy minimization framework to reconstruct the compensated row-column ultrasound imagery. To explore the efficacy of the proposed concept, we introduced three different realizations of the proposed compensated row-column ultrasound imaging system. First, we introduce a compensated row-column imaging system based on a novel multilayered conditional random field driven framework to better account for local spatial relationships in the captured data. Second, we incorporated more global relationships by introducing a compensated row-column imaging system based around a novel edge-guided stochastically fully connected random field framework. Third, accounting for the case where the analytical image formation model may not optimally reflect the real-world physical system, we introduce a compensated row-column imaging system based around a data-driven spatially varying point-spread-function learning framework to better characterize the true physical image formation characteristics. While these different realizations of the compensated row-column system have their advantages and disadvantages, which will be discussed throughout this thesis, they all manage to boost the performance of the row-column method to comparable and often higher levels than the fully addressed 2-D array

The SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation

This Synthetic Aperture Radar (SAR) handbook of applied methods for forest monitoring and biomass estimation has been developed by SERVIR in collaboration with SilvaCarbon to address pressing needs in the development of operational forest monitoring services. Despite the existence of SAR technology with all-weather capability for over 30 years, the applied use of this technology for operational purposes has proven difficult. This handbook seeks to provide understandable, easy-to-assimilate technical material to remote sensing specialists that may not have expertise on SAR but are interested in leveraging SAR technology in the forestry sector

Interferometric Synthetic Aperture RADAR and Radargrammetry towards the Categorization of Building Changes

The purpose of this work is the investigation of SAR techniques relying on multi image acquisition for fully automatic and rapid change detection analysis at building level. In particular, the benefits and limitations of a complementary use of two specific SAR techniques, InSAR and radargrammetry, in an emergency context are examined in term of quickness, globality and accuracy. The analysis is performed using spaceborne SAR data

Monitoring crops water needs at high spatio-temporal resolution by synergy of optical/thermal and radar observations

L'optimisation de la gestion de l'eau en agriculture est essentielle dans les zones semi-arides afin de préserver les ressources en eau qui sont déjà faibles et erratiques dues à des actions humaines et au changement climatique. Cette thèse vise à utiliser la synergie des observations de télédétection multispectrales (données radar, optiques et thermiques) pour un suivi à haute résolution spatio-temporelle des besoins en eau des cultures. Dans ce contexte, différentes approches utilisant divers capteurs (Landsat-7/8, Sentinel-1 et MODIS) ont été developpées pour apporter une information sur l'humidité du sol (SM) et le stress hydrique des cultures à une échelle spatio-temporelle pertinente pour la gestion de l'irrigation. Ce travail va parfaitement dans le sens des objectifs du projet REC "Root zone soil moisture Estimates at the daily and agricultural parcel scales for Crop irrigation management and water use impact: a multi-sensor remote sensing approach" (http://rec.isardsat.com/) qui visent à estimer l'humidité du sol dans la zone racinaire (RZSM) afin d'optimiser la gestion de l'eau d'irrigation. Des approches innovantes et prometteuses sont mises en place pour estimer l'évapotranspiration (ET), RZSM, la température de surface du sol (LST) et le stress hydrique de la végétation à travers des indices de SM dérivés des observations multispectrales à haute résolution spatio-temporelle. Les méthodologies proposées reposent sur des méthodes basées sur l'imagerie, la modélisation du transfert radiatif et la modélisation du bilan hydrique et d'énergie et sont appliquées dans une région à climat semi-aride (centre du Maroc). Dans le cadre de ma thèse, trois axes ont été explorés. Dans le premier axe, un indice de RZSM dérivé de LST-Landsat est utilisé pour estimer l'ET sur des parcelles de blé et des sols nus. L'estimation par modélisation de ET a été explorée en utilisant l'équation de Penman-monteith modifiée obtenue en introduisant une relation empirique simple entre la résistance de surface (rc) et l'indice de RZSM. Ce dernier est estimé à partir de la température de surface (LST) dérivée de Landsat, combinée avec les températures extrêmes (en conditions humides et sèches) simulée par un modèle de bilan d'énergie de surface piloté par le forçage météorologique et la fraction de couverture végétale dérivée de Landsat. La méthode utilisée est calibrée et validée sur deux parcelles de blé situées dans la même zone près de Marrakech au Maroc. Dans l'axe suivant, une méthode permettant de récupérer la SM de la surface (0-5 cm) à une résolution spatiale et temporelle élevée est développée à partir d'une synergie entre données radar (Sentinel-1) et thermique (Landsat) et en utilisant un modèle de bilan d'énergie du sol. L'approche développée a été validée sur des parcelles agricoles en sol nu et elle donne une estimation précise de la SM avec une différence quadratique moyenne en comparant à la SM in situ, égale à 0,03 m3 m-3. Dans le dernier axe, une nouvelle méthode est développée pour désagréger la MODIS LST de 1 km à 100 m de résolution en intégrant le SM proche de la surface dérivée des données radar Sentinel-1 et l'indice de végétation optique dérivé des observations Landsat. Le nouvel algorithme, qui inclut la rétrodiffusion S-1 en tant qu'entrée dans la désagrégation, produit des résultats plus stables et robustes au cours de l'année sélectionnée. Dont, 3,35 °C était le RMSE le plus bas et 0,75 le coefficient de corrélation le plus élevé évalués en utilisant le nouvel algorithme.Optimizing water management in agriculture is essential over semi-arid areas in order to preserve water resources which are already low and erratic due to human actions and climate change. This thesis aims to use the synergy of multispectral remote sensing observations (radar, optical and thermal data) for high spatio-temporal resolution monitoring of crops water needs. In this context, different approaches using various sensors (Landsat-7/8, Sentinel-1 and MODIS) have been developed to provide information on the crop Soil Moisture (SM) and water stress at a spatio-temporal scale relevant to irrigation management. This work fits well the REC "Root zone soil moisture Estimates at the daily and agricultural parcel scales for Crop irrigation management and water use impact: a multi-sensor remote sensing approach" (http://rec.isardsat.com/) project objectives, which aim to estimate the Root Zone Soil Moisture (RZSM) for optimizing the management of irrigation water. Innovative and promising approaches are set up to estimate evapotranspiration (ET), RZSM, land surface temperature (LST) and vegetation water stress through SM indices derived from multispectral observations with high spatio-temporal resolution. The proposed methodologies rely on image-based methods, radiative transfer modelling and water and energy balance modelling and are applied in a semi-arid climate region (central Morocco). In the frame of my PhD thesis, three axes have been investigated. In the first axis, a Landsat LST-derived RZSM index is used to estimate the ET over wheat parcels and bare soil. The ET modelling estimation is explored using a modified Penman-Monteith equation obtained by introducing a simple empirical relationship between surface resistance (rc) and a RZSM index. The later is estimated from Landsat-derived land surface temperature (LST) combined with the LST endmembers (in wet and dry conditions) simulated by a surface energy balance model driven by meteorological forcing and Landsat-derived fractional vegetation cover. The investigated method is calibrated and validated over two wheat parcels located in the same area near Marrakech City in Morocco. In the next axis, a method to retrieve near surface (0-5 cm) SM at high spatial and temporal resolution is developed from a synergy between radar (Sentinel-1) and thermal (Landsat) data and by using a soil energy balance model. The developed approach is validated over bare soil agricultural fields and gives an accurate estimates of near surface SM with a root mean square difference compared to in situ SM equal to 0.03 m3 m-3. In the final axis a new method is developed to disaggregate the 1 km resolution MODIS LST at 100 m resolution by integrating the near surface SM derived from Sentinel-1 radar data and the optical-vegetation index derived from Landsat observations. The new algorithm including the S-1 backscatter as input to the disaggregation, produces more stable and robust results during the selected year. Where, 3.35 °C and 0.75 were the lowest RMSE and the highest correlation coefficient assessed using the new algorithm