This thesis further develops a method from ongoing thesis projects with the goal of generating images using synthetic aperture radar (SAR) simulations coded in MATLAB. The project is supervised by Dr. John Saghri and sponsored by Raytheon Space and Airborne Systems. SAR is a type of imaging radar in which the relative movement of the antenna with respect to the target is utilized. Through the simultaneous processing of the radar reflections over the movement of the antenna via the range Doppler algorithm (RDA), the superior resolution of a theoretical wider antenna, termed synthetic aperture, is obtained. The long term goal of this ongoing project is to develop a simulation in which realistic SAR images can be generated and used for SAR Automatic Target Recognition (ATR). Current and past Master’s theses on ATR were restricted to a small data set of Man-portable Surveillance and Target Acquisition Radar (MSTAR) images as most SAR images for military ATR are not released for public use. Also, with an in-house SAR image generation scheme the parameters of noise, target orientation, the elevation angle or look angle to the antenna from the target and other parameters can be directly controlled and modified to best serve ATR purposes or other applications such as three-dimensional SAR holography. At the start of the project in September 2007, the SAR simulation from previous Master’s theses was capable of simulating and imaging point targets in a two dimensional plane with limited mobility. The focus on improvements to this simulation through the course of this project was to improve the SAR simulation for applications to more complex two-dimensional targets and simple three-dimensional targets, such as a cube. The input to the simulation uses a selected two-dimensional, grayscale target image and generates from the input a two-dimensional target profile of reflectivity over the azimuth and range based on the intensity of the pixels in the target image. For three-dimensional simulations, multiple two-dimensional azimuth/range profiles are imported at different altitudes. The output from both the two-dimensional and three-dimensional simulations is the SAR simulated and RDA processed image of the input target profile. Future work on this ongoing project will include an algorithm to calculate line of sight limitations of point targets and processing optimization of the radar information generation implemented in the code so that more complex and realistic targets can be simulated and imaged using SAR for applications in ATR and 3D SAR holography.

Every year, the number of applications relying on information extracted from high-resolution satellite imagery increases. In particular, the combined use of different data sources is rising steadily, for example to create high-resolution maps, to detect changes over time or to conduct image classification. In order to correctly fuse information from multiple data sources, the utilized images have to be precisely geometrically registered and have to exhibit a high absolute geo-localization accuracy. Due to the image acquisition process, optical satellite images commonly have an absolute geo-localization accuracy in the order of meters or tens of meters only. On the other hand, images captured by the high-resolution synthetic aperture radar satellite TerraSAR-X can achieve an absolute geo-localization accuracy within a few decimeters and therefore represent a reliable source for absolute geo-localization accuracy improvement of optical data. The main objective of this thesis is to address the challenge of image matching between high resolution optical and synthetic aperture radar (SAR) satellite imagery in order to improve the absolute geo-localization accuracy of the optical images. The different imaging properties of optical and SAR data pose a substantial challenge for a precise and accurate image matching, in particular for the handcrafted feature extraction stage common for traditional optical and SAR image matching methods. Therefore, a concept is required which is carefully tailored to the characteristics of optical and SAR imagery and is able to learn the identification and extraction of relevant features. Inspired by recent breakthroughs in the training of neural networks through deep learning techniques and the subsequent developments for automatic feature extraction and matching methods of single sensor images, two novel optical and SAR image matching methods are developed. Both methods pursue the goal of generating accurate and precise tie points by matching optical and SAR image patches. The foundation of these frameworks is a semi-automatic matching area selection method creating an optimal initialization for the matching approaches, by limiting the geometric differences of optical and SAR image pairs. The idea of the first approach is to eliminate the radiometric differences between the images trough an image-to-image translation with the help of generative adversarial networks and to realize the subsequent image matching through traditional algorithms. The second approach is an end-to-end method in which a Siamese neural network learns to automatically create tie points between image pairs through a targeted training. The geo-localization accuracy improvement of optical images is ultimately achieved by adjusting the corresponding optical sensor model parameters through the generated set of tie points. The quality of the proposed methods is verified using an independent set of optical and SAR image pairs spread over Europe. Thereby, the focus is set on a quantitative and qualitative evaluation of the two tie point generation methods and their ability to generate reliable and accurate tie points. The results prove the potential of the developed concepts, but also reveal weaknesses such as the limited number of training and test data acquired by only one combination of optical and SAR sensor systems. Overall, the tie points generated by both deep learning-based concepts enable an absolute geo-localization improvement of optical images, outperforming state-of-the-art methods.

L’imagerie RSO circulaire aéroportée permet d’obtenir de nombreuses informations sur les zones imagées et sur les cibles mobiles. Les objets peuvent être observés sous plusieurs angles, et l’illumination continue d’une même scène permet de générer plusieurs images successives de la même zone. L’objectif de cette thèse est de développer une méthode de reconstruction de trajectoire de cibles mobiles en imagerie RSO circulaire monovoie, et d’étudier les performances de la méthode proposée. Nous avons tout d’abord mesuré les coordonnées apparentes des cibles mobiles sur les images RSO et leur paramètre de défocalisation. Ceci permet d’obtenir des informations de mouvement des cibles, notamment de vitesse et d’accélération. Nous avons ensuite utilisé ces mesures pour définir un système d’équations non-linéaires permettant de faire le lien entre les trajectoires réelles des cibles mobiles et leurs trajectoires apparentes. Par une analyse mathématique et numérique de la stabilité de ce système, nous avons montré que seul un modèle de cible mobile avec une vitesse constante permet de reconstruire précisément les trajectoires des cibles mobiles, sous réserve d’une excursion angulaire suffisante. Par la suite, nous avons étudié l’influence de la résolution des images sur les performances de reconstruction des trajectoires, en calculant théoriquement les précisions de mesure et les précisions de reconstruction qui en découlent. Nous avons mis en évidence l’existence théorique d’une résolution azimutale optimale, dépendant de la radiométrie des cibles et de la validité des modèles étudiés. Finalement nous avons validé la méthode développée sur deux jeux de données réelles. / Circular SAR imagery brings a lot of information concerning the illuminated scenes and the moving targets. Objects may be seen from any angle, and the continuity of the illumination allows generating a lot of successive images from the same scene. In the scope of this thesis, we develop a moving target trajectory reconstruction methodology using circular SAR imagery, and we study the performances of this methodology. We have first measured the apparent coordinates of the moving targets on SAR images, and also the defocusing parameter of the targets. This enables us to obtain information concerning target movement, especially the velocity and the acceleration. We then used these measurements to develop a non-linear system that makes the link between the apparent trajectories of the moving targets and the real ones. We have shown, by a mathematical and numerical analysis of the robustness, that only a model of moving target with constant velocity enables us to obtain accurate trajectory reconstructions from a sufficient angular span. Then, we have studied the azimuth resolution influence on the reconstruction accuracy. In order to achieve this, we have theoretically estimated the measurement accuracy and the corresponding reconstruction accuracy. We have highlighted the existence of an optimal azimuth resolution, depending on the target radiometry and on the validity of the two target models. Finally, we have validated the method on two real data sets on X-Band acquired by SETHI and RAMSES NG, the ONERA radar systems, and confirmed the theoretical analyses of its performances.

Mapping of earth resources, environmental monitoring, and many other systems require high-resolution wide-area imaging. Since images often have to be captured at night or in inclement weather conditions, a capability is provided by Synthetic Aperture Radar (SAR). SAR systems exploit radar signal's long-range propagation and utilize digital electronics to process complex information, all of which enables high-resolution imagery. This gives SAR systems advantages over optical imaging systems, since, unlike optical imaging, SAR is effective at any time of day and in any weather conditions. Moreover, advanced technology called Interferometric Synthetic Aperture Radar (InSAR), has the potential to apply phase information from SAR images and to measure ground surface deformation. However, given the current state of technology, the quality of InSAR data can be distorted by several factors, such as image co-registration, interferogram generation, phase unwrapping, and geocoding. Image co-registration aligns two or more images so that the same pixel in each image corresponds to the same point of the target scene. Super-Resolution (SR), on the other hand, is the process of generating high-resolution (HR) images from a low-resolution (LR) one. SR influences the co-registration quality and therefore could potentially be used to enhance later stages of SAR image processing. Our research resulted in two major contributions towards the enhancement of SAR processing. The first one is a new learning-based SR model that can be applied with SAR, and similar applications. A second major contribution is utilizing the devised model for improving SAR co-registration and InSAR interferogram generation, together with methods for evaluating the quality of the resulting images. In the case of phase unwrapping, the process of recovering unambiguous phase values from a two-dimensional array of phase values known only modulo $2\pi$ rad, our research produced a third major contribution. This third major contribution is the finding that quantum annealers can resolve problems associated with phase unwrapping. Even though other potential solutions to this problem do currently exist - based on network programming for example - network programming techniques do not scale well to larger images. We were able to formulate the phase unwrapping problem as a quadratic unconstrained binary optimization (QUBO) problem, which can be solved using a quantum annealer. Since quantum annealers are limited in the number of qubits they can process, currently available quantum annealers do not have the capacity to process large SAR images. To resolve this limitation, we developed a novel method of recursively partitioning the image, then recursively unwrapping each partition, until the whole image becomes unwrapped. We tested our new approach with various software-based QUBO solvers and various images, both synthetic and real. We also experimented with a D-Wave Systems quantum annealer, the first and only commercial supplier of quantum annealers, and we developed an embedding method to map the problem to the D-Wave 2000Q_6, which improved the result images significantly. With our method, we were able to achieve high-quality solutions, comparable to state-of-the-art phase-unwrapping solvers. / Graduate

Automatic Target Recognition (ATR) is a subject involving the use of sensor data to develop an algorithm for identifying targets of significance. It is of particular interest in military applications such as unmanned aerial vehicles and missile tracking systems. This thesis develops an orientation-based classification approach from previous ATR algorithms for 2-D Synthetic Aperture Radar (SAR) images. Prior work in ATR includes Chessa Guilas’ Hausdorff Probabilistic Feature Analysis Approach in 2005 and Daniel Cary’s Optimal Rectangular Fit in 2007. A system incorporating multiple modules performing different tasks is developed to streamline the data processing of previous algorithms. Using images from the publicly available Moving and Stationary Target Acquisition and Recognition (MSTAR) database, target orientation was determined to be the best feature for ATR. A rotationally variant algorithm taking advantage of the combination of target orientation and pixel location for classification is proposed in this thesis. Extensive classification results yielding an overall accuracy of 76.78% are presented to demonstrate algorithm functionality.

This thesis presents a design methodology for continuous wave (CW) synthetic aperture radar (SAR) systems. The focus is on design considerations specific to small, low-power systems suitable for operation on small aircraft and unmanned aerial vehicles (UAVs). Well-known results which have been derived in other works, such as the radar equation, are explained in the context of low-power, CW systems. Additionally, design issues unique to CW SAR are addressed and the results generalized. A method for controlling feedthrough between antennas is developed, and the resulting limitations on swath width are discussed. Methods are developed which allow an engineer to design a CW SAR system to obtain a given swath width, resolution, and data rate, and necessary tradeoffs are discussed. Using the proposed methodology, designs for two specific SAR systems are developed. Example sections outline the design of two small SAR systems called microASAR and microBSAR. These sections present a real-world application of the methodology and offer explanations of the rationale behind many of the design choices. Straightforward methods for testing different aspects of a completed SAR system are developed and presented. These procedures are carried out using microASAR hardware, and the results are used to validate the design methodology.

This thesis presents the development of a high-resolution ground-based 3D-SAR system and investigates its application to microwave-vegetation studies. The development process of the system is detailed including an enumeration of high-level requirements, discussions on key design issues, and detailed descriptions of the system down to a component level. The system operates on a 5.4 GHz (C-band) signal, provides a synthetic aperture area of 1.7 m x 1.7 m, and offers resolution of 0.75 m x 0.3 m x 0.3 m (range x azimuth x elevation). The system is employed on several trees with varying physical characteristics. The resulting imagery demonstrates successful 3D reconstruction of the trees and some of their internal features. The individual leaves and small branches are not visible due to the system resolution and the size of the wavelength. The foliage's outline and internal density distribution is resolved. Large branches are visible where geometry is favorable. Trunks are always visible due to their size and normal-facing incidence surface and their return has the strongest contribution from their base. The imagery is analyzed for dependencies on radar and tree parameters including: incidence angle, signal frequency, polarization, inclusion size, water content, and species. In the current work, a single frequency (5.4 GHz) and polarization (HH) is used which leaves the door open for future analysis to use other frequencies and polarizations. The improved resolution capabilities of the 3D-SAR system enables more precise backscatter measurements leading to a greater understanding of microwave-vegetation scattering behavior.

Synthetic Aperture Radar (SAR) is a type of radar system that can generate high-resolution images with which one can detect subtle changes on the scale of centimetres from space. It can operate in any weather condition and during both day and night, making it unique compared to optical sensors. SAR is used for applications such as environmental monitoring, surveillance, and earth observation. Its ability to penetrate clouds and, to some extent, vegetation, allows for insights into terrain, vegetation structure, and even subsurface features. The importance of modelling the generated data of a SAR system before initiating the construction and development of it cannot be overstated. This thesis presents the implementation of the Reverse BackProjection Algorithm (RBPA) designed to generate raw SAR data efficiently and accurately. The RBPA stands out with its flexibility, enabling researchers and designers to simulate and gauge the SAR system's effectiveness under diverse scenarios. This provides an easy way of fine-tuning configurations for distinct needs concerning scene geometries, orbits, and radar designs. Two versions of the RBPA were implemented, differing slightly in the theoretical approach of azimuth defocusing. On top of this, a bistatic mode and Terrain Observation by Progressive Scans (TOPS) acquisition mode was also implemented. The inclusion of these two modes were specifically due to their relevance for the upcoming European Space Agency (ESA) SAR mission, Harmony. The addition of the TOPS mode required a comprehensive design of the antenna framework. Moreover, this implementation also paves the way for simpler integration of modes in the future. The two versions of the RBPA were profiled, revealing the optimal system and parameter configurations.

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.

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