Deviations from a nominal, straight-line flight path of a synthetic aperture radar (SAR) lead to inaccurate and defocused radar images. This thesis is an investigation into the improvement of the motion compensation algorithm created for the BYU inteferometric synthetic aperture radar, YINSAR. The existing BYU SAR processing algorithm produces improved radar imagery but does not fully account for variations in attitude (roll, pitch, yaw) and does not function well with large position deviations. Results in this thesis demonstrate that a higher order motion compensation algorithm is not as effective as using a segmented reference track, coupled with the current lower-order motion compensation algorithm. Attitude variations cause a Doppler shift and are corrected by limiting the processed azimuth bandwidth or by reversing the frequency shift with a range-dependent filter. Another important area considered is the effects of motion compensation on interferometry. When performing interferometry with YINSAR, motion compensating both channels to a single track has two effects. First, the applied MOCO phase corrections remove the "flat-earth" differential phase from the interferogram. Second, range resampling coregisters the two images. All of these changes have helped to improve YINSAR imagery.

In this thesis, the Omega-k algorithm used for processing stripmap synthetic aperture radar (SAR) data is explored in detail. While the original Omega-k algorithm does not achieve the same SNR as a matched filter, a modification is presented which enables the algorithm to nearly achieve that SNR. It is shown that the focused point spread function obtained when the Omega-k algorithm is used differs in important ways from the output of a modified version of the matched filter. Spread out sidelobes and a stretched mainlobe are observed when the data is processed by the Omega-k algorithm. These differences may increase the potential interference between some nearby scatterers; however, the amplitude of the resulting sidelobes is lower than that observed for the matched filter, and the potential interference between other nearby scatterers is reduced. The details of a discrete implementation of the algorithm are also presented. Two methods for mixing the frequency domain signal to baseband are compared, and one is shown to potentially reduce the required accuracy of the interpolation kernel. Finally, the errors associated with the key approximation used by the algorithm are explored through simulation, and it is shown that the approximation is sufficiently accurate for a particularly demanding configuration.

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.

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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