Synthetic aperture radar (SAR) systems are versatile, high-resolution radar imagers useful for providing detailed intelligence, surveillance, and reconnaissance, especially when atmospheric conditions are non-ideal for optical imagers. However, moving targets in SAR images are smeared. Along-track interferometry is a commonly-used method for extracting the motion parameters of moving targets but requires a dual-aperture SAR system, which may be power- size- or cost-prohibitive. This thesis presents a method of estimating target motion parameters in single-channel SAR data given geometric target motion constraints. I test this method on both simulated and actual SAR data. This estimation method includes an initial estimate, computation of the SAR ambiguity function, and application of the target motion constraints to form a focused image of the moving target. The constraints are imposed by assuming that target motion is restricted to a road. Finally, I measure its performance by investigating the error introduced in the motion estimates using both simulated and actual data.

Prior to the wide deployment of robotic systems, they must be able to navigate autonomously. These systems cannot rely on good weather or daytime navigation and they must also be able to navigate in unknown environments. All of this must take place without human interaction. A majority of modern autonomous systems rely on GPS for position estimation. While GPS solutions are readily available, GPS is often lost and may even be jammed. To this end, a significant amount of research has focused on GPS-denied navigation. Many GPS-denied solutions rely on known environmental features for navigation. Others use vision sensors, which often perform poorly at high altitudes and are limited in poor weather. In contrast, radar systems accurately measure range at high and low altitudes. Additionally, these systems remain unaffected by inclimate weather. This dissertation develops the use of radar odometry for GPS-denied navigation. Using the range progression of unknown environmental features, the aircraft's motion is estimated. Results are presented for both simulated and real radar data. In Chapter 2 a greedy radar odometry algorithm is presented. It uses the Hough transform to identify the range progression of ground point-scatterers. A global nearest neighbor approach is implemented to perform data association. Assuming a piece-wise constant heading assumption, as the aircraft passes pairs of scatterers, the location of the scatterers are triangulated, and the motion of the aircraft is estimated. Real flight data is used to validate the approach. Simulated flight data explores the robustness of the approach when the heading assumption is violated. Chapter 3 explores a more robust radar odometry technique, where the relatively constant heading assumption is removed. This chapter uses the recursive-random sample consensus (R-RANSAC) Algorithm to identify, associate, and track the point scatterers. Using the measured ranges to the tracked scatterers, an extended Kalman filter (EKF) iteratively estimates the aircraft's position in addition to the relative locations of each reflector. Real flight data is used to validate the accuracy of this approach. Chapter 4 performs observability analysis of a range-only sensor. An observable, radar odometry approach is proposed. It improves the previous approaches by adding a more robust R-RANSAC above ground level (AGL) tracking algorithm to further improve the navigational accuracy. Real flight results are presented, comparing this approach to the techniques presented in previous chapters.

Urban represents one of the most dynamic areas in the global change context. To support rational policies for sustainable urban development, remote sensing technologies such as Synthetic Aperture Radar (SAR) enjoy increasing popularity for collecting up-to-date and reliable information such as urban land cover/land-use. With the launch of advanced spaceborne SAR sensors such as RADARSAT-2, multitemporal fully polarimetric SAR data in high-resolution become increasingly available. Therefore, development of new methodologies to analyze such data for detailed and accurate urban mapping is in demand.   This research investigated multitemporal fine resolution spaceborne polarimetric SAR (PolSAR) data for detailed urban land cover mapping. To this end, the north and northwest parts of the Greater Toronto Area (GTA), Ontario, Canada were selected as the study area. Six-date C-band RADARSAT-2 fine-beam full polarimetric SAR data were acquired during June to September in 2008. Detailed urban land covers and various natural classes were focused in this study.   Both object-based and pixel-based classification schemes were investigated for detailed urban land cover mapping. For the object-based approaches, Support Vector Machine (SVM) and rule-based classification method were combined to evaluate the classification capacities of various polarimetric features. Classification efficiencies of various multitemporal data combination forms were assessed. For the pixel-based approach, a temporal-spatial Stochastic Expectation-Maximization (SEM) algorithm was proposed. With an adaptive Markov Random Field (MRF) analysis and multitemporal mixture models, contextual information was explored in the classification process. Moreover, the fitness of alternative data distribution assumptions of multi-look PolSAR data were compared for detailed urban mapping by this algorithm.   Both the object-based and pixel-based classifications could produce the finer urban structures with high accuracy. The superiority of SVM was demonstrated by comparison with the Nearest Neighbor (NN) classifier in object-based cases. Efficient polarimetric parameters such as Pauli parameters and processing approaches such as logarithmically scaling of the data were found to be useful to improve the classification results. Combination of both the ascending and descending data with appropriate temporal span are suitable for urban land cover mapping. The SEM algorithm could preserve the detailed urban features with high classification accuracy while simultaneously overcoming the speckles. Additionally the fitness of the G0p and Kp distribution assumptions were demonstrated better than the Wishart one. / <p>QC 20110315</p>

The Synthetic Aperture Radar (SAR) algorithm processes multiple radar returns from the target space to generate a single high-resolution image. Targets moving through the target space during the capture sequence will appear distorted on the final image. In addition, there is no velocity information that is calculated as part of the processing. The objective of this thesis is to develop techniques to determine the azimuth and range velocities of moving objects in the target space in the early stages of SAR processing. The typical SAR processing steps are Range Compressed, Range Doppler, and final image generation. The range velocity of a target can be determined after the Range Compression stage, and the azimuth velocity can be determined after the Range Doppler image is created. Calculating the velocity of a target without performing all the steps of the SAR process allows such information can be obtained quicker than the final image. This work is done as part of Cal Poly’s SAR Automatic Target Recognition (ATR) project, sponsored by Raytheon Space and Airborne Systems Division and headed by Professor John Saghri. The simulations performed as part of this thesis are done in a MATLAB simulation environment implementing a two-dimension SAR target space, first introduced in Brian Zaharris’ thesis. This work has expanded on this environment by introducing point target azimuth and range velocity detection.

Synthetic Aperture Radar (SAR) systems generate two dimensional images of a target area using RF energy as opposed to light waves used by cameras. When cloud cover or other optical obstructions prevent camera imaging over a target area, SAR can be substituted to generate high resolution images. Linear frequency modulated signals are transmitted and received while a moving imaging platform traverses a target area to develop high resolution images through modern digital signal processing (DSP) techniques. The motivation for this joint thesis project is to design and construct a scaled SAR system to support Cal Poly radar projects. Objectives include low-cost, high resolution SAR architecture development for capturing images in desired target areas. To that end, a scaled SAR system was successfully designed, built, and tested. The current SAR system, however, does not perform azimuthal compression and range cell migration correction (image blur reduction). These functionalities can be pursued by future students joining the ongoing radar project. The SAR system includes RF modulating, demodulating, and amplifying circuitry, broadband antenna design, movement platform, LabView system control, and MATLAB signal processing. Each system block is individually described and analyzed followed by final measured data. To confirm system operation, images developed from data collected in a single target environment are presented and compared to the actual configuration.

This thesis explores the use and accuracy of several discrete-time image filters for the purpose of target tracking in Synthetic Aperture Radar imagery. Both extended targets and point targets are used for tracking, showing the need for different types of filters for each target type. Monte Carlo analysis is performed on the results of the extended target filter results to determine the absolute mean-squared error between the filter prediction of the target centroid and the actual location of the target centroid. Two different filters were chosen for the extended target: Kalman and H Infinity. Both the Kalman and H Infinity filters perform tracking by accurately estimating the state of the dynamic system, and in some cases it may be useful to simulate a situation when a target temporarily disappears from radar view. The ability of both filters to predict target location with no input measurements is investigated. A unique trait of the H Infinity filter is its ability to accurately and efficiently estimate the state of a dynamic system given no information about the noise environment. To simulate more realistic targets, smaller circular and square targets are created and a sensitivity analysis is performed using the Kalman and H Infinity filters to determine the shortfalls of these filter techniques as targets become smaller and smaller. The results indicate that these tracking methods are no longer useful as the targets become so small that they approach being only a single pixel in size. A new filter called the Prediction and Matching Detection (PAMD) filter is used for single-pixel point targets. This filter illustrates the importance of having very high frame rate images with little change in velocity over consecutive frames if choosing to use the PAMD algorithm. The PAMD filter is extended to track more than one target at a time. Tracking of raw SAR data is preferred over post-processed images due to the decreased amount of processing time. The Kalman and H Infinity filters are implemented to track raw radar data during its first 3 seconds of motion in 2-dimensions by accounting for the measurements of two parameters: the squint angle and slant range. Noise is added to the measurements to simulate platform inaccuracies. The project is a continuation of prior SAR research at Cal Poly under Dr. John Saghri with the sponsorship of Raytheon Space & Airborne Systems.

Floods are the most common global natural disaster, and 1 billion people live in floodplains worldwide adding to the impactful damage that inundation causes. Disaster managers strive to mitigate damages to their communities but need to know what the impact of a potential flood may be. GEOGloWS ECMWF Streamflow Services estimates forecasted streamflow around the world. These forecasted streamflow's can be used to create predicted flood extent maps using Height Above Nearest Drainage (HAND) or Sedimentation and River Hydraulics - Two Dimension (SRH-2D). Another method to obtain a flood map is using Setinel-1 satellite Synthetic Aperture Radar (SAR) imagery. Flood maps alone will not demonstrate the impact of the flood, but some exposure data will provide needed impact metrics. In this research, I wanted to produce a general geoprocessing method for stakeholders to compute flood impact metrics over any flood extent map using any exposure dataset. Additionally, I sought to create similar geoprocessing workflows in ArcGIS Pro, QGIS, and stand-alone Python script so that the stakeholders can choose the best suited method that correlates with their access and familiarity. The general geoprocessing workflow was tested using three different global exposure datasets (Agriculture, Infrastructure, and Population). The three different geoprocessing implementations were tested in three areas that are of concern in the greater NASA SERVIR organization using the same flood map and exposure datasets for each area. This research produced a feasible, sustainable, successful, generalized geoprocessing workflow that computes flood impact metrics from a flood map and global exposure datasets. The global datasets can be interchanged with higher resolution exposure datasets specific to an area of interest generating more accurate results. The three geoprocessing methods performed similarly. The results were slightly different when the exposure dataset was a raster file as the conversion from raster to vector format produced differences in rounding values and programming implementation. However, this research's findings are such that the three geoprocessing methods are comparable and that any of the three geoprocessing implementations will produce reasonably similar flood impact results. Ongoing work by the Brigham Young University (BYU) Hydroinformatics lab is to create a Tethys web application that will allow stakeholders to view the flood map and flood impact of areas of interest. Future work may include investigating the workflow workability on a global scale, discovering and implementing global exposure data sources of better resolution, researching more data metrics that can contribute to a more robust flood impact results, and increasing the accuracy of flood impact results when compared among ArcGIS Pro, QGIS, and Python.

Antarctica is home to numerous perennially ice-covered (PIC) lakes that host rich benthic microbial ecosystems. These lakes are covered by a thick floating ice cover year-round and often have water columns supersaturated in dissolved gases, resulting in heavily bubbled ice covers, altering the optical properties of the ice and the amount of light that penetrates into the water column. Thus, understanding the optical properties of perennial lake ice can have important scientific implications to the study of life on Earth and the search for extraterrestrial life. Synthetic aperture radar (SAR) remote sensing has been used rigorously for over 50 years to study and monitor the seasonal response and long-term trends of backscatter over seasonally ice-covered (SIC) Arctic lakes. Limited studies have assessed the impacts of dissolved gases and ice/water interface bubbles on SAR backscatter variability over SIC lakes. The seasonal backscatter response of Antarctic PIC lakes remains unexplored; their physical nature asserts that their backscatter response should largely be decoupled from seasonal factors according to SIC lake backscatter theory. Additionally, gas supersaturated PIC lakes are ideal candidates to better understand the role of gas buildup and bubble formation on the backscatter response from floating ice covers. This thesis leverages a dense stack of Sentinel-1 C-band SAR imagery over Lake Untersee, a well-sealed PIC lake in East Antarctica, to explore the relationships between SAR backscatter and ice/water interface bubbles. This analysis integrates field measurements and temporal observations at the ice/water interface. Lastly, a brief comparative analysis extends to other ice covers, including moat-forming PIC lakes, as well as first-year and multi-year Arctic sea and lake ice. It is shown that Lake Untersee has a seasonal backscatter regime that is linked to air temperature. A strong correlation is found between the timing of backscatter intensity increase in winter and ice thickness. This relationship is attributed to variations in ice thickness which affect the length of the freezing period under the ice, the rate of dissolved gas accumulation, and ultimately, the nucleation and abundance of bubbles at the ice/water interface. These findings can be applied to other PIC lakes that have seasonal gas regimes. This research provides valuable insights into the complex interplay between ice cover characteristics, gas dynamics, interface bubbles, and SAR backscatter, enhancing our understanding of polar aquatic ecosystems and their broader implications for global environments.

For some time now, there has been a growing awareness in society about climate change, pollution, energy and the use of natural resources. This thinking has permeated society, mainly because the extreme natural phenomena that we are experiencing nowadays are no longer outliers in our time series of meteorological records. In this regard, it has been proven that the actual high temperatures are not only unparalleled, but also consistent around the globe which is something that had not happened until now (Neukom et al., 2019). The XX century was a turning point when it comes to the increase of the landuse for crops. In a context where the population doubled, the crop production for food from 1960 to 2010 tripled, helping to reduce the hungry population. When the world’s population is expected to continue to grow up to 9 billion people (Goodfray et al., 2010) by middle XXI century, it is essential to provide ourselves with the necessary tools to maximise crop production by taking advantage of all the resources available under a sustainable point of view. Under this context, all farmers in the European Union (EU) have the possibility to benefit from the Common Agricultural Policy (CAP), which came into force in 1960. The CAP is responsible for the financing of aid to farmers on a cross-compliance basis, based on the declaration of crop types. Traditionally, the authorities have checked the veracity of declarations in person through field inspections, which is clearly a highly inefficient, impractical and very expensive system. However, in 2018 the European Commission drafted an amendment to the CAP (European Commission, 2018), to be implemented in 2020, recommending the establishment of newprocedures for checking declarations, including the use of satellite data from the Copernicus programme or other new technologies. Among the various satellite technologies, Synthetic Aperture Radar (SAR) (Brown,1967; Curlander and McDonough, 1992) has proven the most reliable,as the images are acquired with a constant pass period and they are not subject to cloud problems (as is the case with sensors working in the optical domain) and information can be acquired both day and night. They are based in a SAR microwave sensor installed on a satellite platform with a forward trajectory which offers side-looking imaging geometries. Working in a range between 300 MHz and 30 GHz, the SAR sensor is in charge of emitting electromagnetic pulses and receiving the resulting echoes from the imaged target, which can help retrieve information about its dielectric properties, geometry, orientation, shape, and its behaviour along time. For a given target, the SAR backscattering response σ0 is function of many parameters (Lee and Pottier, 2017; Dobson et al., 1985): wave frequency, polarisation, imaging configuration, roughness, geometrical structure and dielectric properties. This makes the information extraction a major problem, as identical radar responses from two different targets may lead to the same result. To cope with this problem, the main techniques are based on extending the observation space by working with the full diversity of data. Thus, the main axes of SAR data are: • Time • Polarimetry • Interferometry • Frequency. Time series of radar data constitutes a major source of information for the classification of crops and land cover, since it makes it possible to distinguish between classes by their temporal behaviour: some land covers show a uniform response along time (e.g. urban areas), whereas there are others subject to seasonal changes (e.g. crops). It may happen that different crop species give the same radar response at a given time, however, when the time window becomes larger, and consecutive acquisitions are taken over a shorter time span, the more one can detect abrupt changes in the target over a longer time interval. Polarimetry is sensitive to the shape, orientation and the scattering mechanisms of the scatterers (Boerner et al.,1981; Zyl, Zebker, and Elachi, 1987). In that sense, when using different polarisations it is possible to discern better the true nature of the target, as some features may be visible in one polarisation but not in the others. Regarding multi-spectral data, it also constitutes a major source of information which can be exploited for classification purposes. Working with sensors operating at different frequencies, or wavelengths, provides diversity in the size of the elements of the scene to which the radar is sensitive as the radar backscattering will come from elements the size of the wavelength used it. For all of the above, multifrequency data provide complementary information, as each frequency operates and interacts with elements of the same wavelength or longer, and being transparent to all others. In addition, different bands are also associated with different spatial resolutions, so a high-frequency sensor can complement the classification performance of a low-frequency sensor when there are sufficiently small details in the scene that cannot be appreciated with the spatial resolution available at the lower frequency. From all the 4 axes exposed above, Interferometry (Graham, 1974) is without a doubt the least exploited for classification purposes. While polarimetry is sensitive to the scattering mechanisms of the scene by means of the polarisation information, interferometry adds the third dimension by being sensitive to the spatial distribution of the scatterers (Treuhaft et al., 1996). Coherence and phase difference computed between two complex-valued SAR images are the main descriptors of interferometry (Bamler and Hartl, 1998), and together, can be used to derive topographic information, vegetation structure, and deformation (volcanoes, landslides, etc.). For this reason, interferometry is especially suited for classification of covers in which there is vertical distribution of elements, e.g. urban areas and vegetation (forests and crops). Polarimetric interferometric SAR (PolInSAR) (Cloude and Papathanassiou, 1998; Treuhaft and Cloude, 1999), constitutes the next step forward, and is based on the application of interferometry to all polarisation channels. Polarimetry can identify the different scattering mechanisms in the scene by using the polarisation information, whilst interferometry is able to locate the effective scattering phase centres, which are mainly dependent on frequency, the polarisation employed, the physical, geometrical structure and orientation of the scatterer. By using the combination of both we can retrieve the vertical structure of the scene, which shows a great potential for classification purposes, since classes characterised by similar backscattering or polarimetric responses can be separated if their heights are different (e.g. types of buildings, forests, crops, etc.), whereas classes with similar heights, and hence similar interferometric coherence values (e.g. grass, crops, bare soil, etc.) can be resolved using their polarimetric response. In summary, PolInSAR-based classification is attractive since polarimetric ambiguities are resolved by interferometric information and vice-versa. The lack of exploitation of the 4 data axes in the literature, plus the arrival of a new generation of SAR sensors in the near future such as ROSE-L, BIOMASS and NISAR among others, offers a new range of possibilities in terms of new types of features for classification whose results and impact must be analysed. In this context, there are many types of SAR data (i.e. features) that have not been used yet, acquired from different sensors (Sentinel-1, PAZ, TanDEMX, TerraSAR-X and ALOS-2), and whose diversity axes, either used individually or jointly, have not yet been explored for classification applications. Therefore, the exploration of these new types of SAR data, whose contribution to classification is unknown regarding crop-type mapping, is the main objective of this doctoral thesis, and consequently also its main novelty. Based on the current state of the art of the research topic the main objective of this PhD thesis is to explore the added value of new SAR features, and their potential, alone or used together, for crop type and land cover classification. In the end, several experiments will be carried out, in different test sites, in which the proposed new features will be evaluated and compared with the traditional observables used so far, with the aim of evaluating their internal potential in classification applications. / Work supported by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Projects TEC2017-85244-C2-1-P and PID2020-117303GB-C22. Mario Busquier received a grant from the University of Alicante UAFPU20-08.

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