Ultrasound is a well-established tool for medical imaging. It is non-invasive and relatively inexpensive, but the severe attenuation caused by propagation through tissue limits its effectiveness for deep imaging. In recent years, the ready availability of fast, inexpensive computer hardware has facilitated the adoption of signal coding and compression techniques to counteract the effects of attenuation. Despite widespread investigation of the topic, published opinions vary as to the relative suitability of discrete-phase-modulated and frequency-modulated (or continuous-phase-modulated) signals for ultrasonic imaging applications. This thesis compares the performance of discrete binary-phase coded pulses to that of frequency-modulated pulses at the higher imaging frequencies at which the effects of attenuation are most severe. The performance of linear and non-linear frequency modulated pulses with optimal side-lobe characteristics is compared to that of complementary binary-phase coded pulses by simulation and experiment. Binary-phase coded pulses are shown to be more robust to the affects of attenuation and non-ideal transducers. The comparatively poor performance of frequency-modulated pulses is explained in terms of the spectral characteristics of the signals and filters required to reduce side-lobes to levels acceptable for imaging purposes. In theory, complementary code sets like bi-phase Golay pairs offer optimum side-lobe performance at the expense of a reduction in frame rate. In practice, misalignment caused by motion in the medium can have a severe impact on imaging performance. A novel motioncompensated imaging algorithm designed to reduce the occurrence of motion artefacts and eliminate the reduction in frame-rate associated with complementary-coding is presented. This is initially applied to conventional sequential-scan B-mode imaging then adapted for use in synthetic aperture B-mode imaging. Simulation results are presented comparing the performance of the motion-compensated sequential-scan and synthetic aperture systems with that of simulated systems using uncoded and frequency-modulated excitation pulses.

In this thesis, multidimensional signal processing techniques to mitigate disturbances in synthetic aperture systems such as radio telescopes are investigated. Here, two computationally efficient three-dimensional (3D) spatio-temporal (ST) finite impulse response (FIR) cone filter bank structures are proposed. Furthermore, a strategy is proposed to design 3D ST FIR frustum filter banks, having double-frustum-shaped passbands oriented along the temporal axis, derived from appropriate 3D ST FIR cone filter banks. Both types of cone and frustum filter banks are almost alias free and provide near-perfect reconstruction. In the proposed cone and frustum filter banks, both temporal and spatial filtering operations can be carried out at a significantly lower rate compared to previously reported 3D ST FIR cone filter banks implying lower power consumption. Furthermore, the proposed cone and frustum filter banks require a significantly lower computational complexity than previously reported 3D ST FIR cone and frustum filter banks. Importantly, this is achieved without deteriorating the improvement in signal-to-interference-plus-noise ratio. A theoretical analysis of brightness distribution (BD) errors caused by parameter perturbations and mismatches among the transfer functions of receivers employed in synthetic aperture systems is presented. First, the BD errors caused by perturbations in the transfer functions of low noise amplifiers (LNAs) and anti-aliasing filters (AAFs) are considered, and the characteristics of the additive BD error and its effects on synthesized BDs are thoroughly analyzed. Second, the conditions that should be satisfied by the transfer functions of digital beamformers to eliminate the BD errors caused by their phase responses are examined. The sufficient condition to eliminate the BD errors is that the transfer functions are matched, and, interestingly, the phase responses are not necessary to be linear. Furthermore, the BD errors caused by typical tolerances of passive L and C elements used to implement the AAFs and those caused by the random variations of gain from LNA to LNA are quantified through numerical simulations. The simulations indicate that substantial BD errors are observed at frequencies that are close to the passband edge of the AAFs. / Graduate

The synthetic aperture radar (SAR) onboard Earth observing satellites has been acknowledged as an integral tool for many applications in monitoring the marine environment. Some of these applications include regional sea-ice monitoring and detection of illegal or accidental oil discharges from ships. Nonetheless, a practicality of the usage of SAR images is greatly hindered by the presence of speckle noises. Such noise must be eliminated or reduced to be utilized in real-world applications to ensure the safety of the marine environment. Thus this thesis presents a novel two-phase total variation optimization segmentation approach to tackle such a challenging task. In the total variation optimization phase, the Rudin-Osher-Fatemi total variation model was modified and implemented iteratively to estimate the piecewise smooth state by minimizing the total variation constraints. In the finite mixture model classification phase, an expectation-maximization method was performed to estimate the final class likelihoods using a Gaussian mixture model. Then a maximum likelihood classification technique was utilized to obtain the final segmented result. For its evaluation, a synthetic image was used to test its effectiveness. Then it was further applied to two distinct real SAR images, X-band COSMO-SkyMed imagery containing verified oil-spills and C-band RADARSAT-2 imagery mainly containing two different sea-ice types to confirm its robustness. Furthermore, other well-established methods were compared with the proposed method to ensure its performance. With the advantage of a short processing time, the visual inspection and quantitative analysis including kappa coefficients and F1 scores of segmentation results confirm the superiority of the proposed method over other existing methods.

Thesis (M.S.)--University of Texas at El Paso, 2007. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Diss. -- Espoo -- Teknillinen korkeakoulu. / Myös verkkojulkaisuna.

Thesis (Ph. D.)--University of Florida, 2001. / Title from first page of PDF file. Document formatted into pages; contains xi, 124 p.; also contains graphics. Vita. Includes bibliographical references (p. 120-123).

Thesis (MA)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: Image classification has long been used in earth observation and is driven by the need for accurate maps to develop conceptual and predictive models of Earth system processes. Synthetic aperture radar (SAR) imagery is used ever more frequently in land cover classification due to its complementary nature with optical data. There is therefore a growing need for reliable, accurate methods for using SAR and optical data together in land use and land cover classifications. However, combining data sets inevitably increases data dimensionality and these large, complex data sets are difficult to handle. It is therefore important to assess the benefits and limitations of using multi-temporal, dual-sensor data for applications such as land cover classification. This thesis undertakes this assessment through four main experiments based on combined RADARSAT-2 and SPOT-5 imagery of the southern part of Reunion Island. In Experiment 1, the use of feature selection for dimensionality reduction was considered. The rankings of important features for both single-sensor and dual-sensor data were assessed for four dates spanning a 6-month period, which coincided with both the wet and dry season. The mean textural features produced from the optical bands were consistently ranked highly across all dates. In the two later dates (29 May and 9 August 2014), the SAR features were more prevalent, showing that SAR and optical data have complementary natures. SAR data can be used to separate classes when optical imagery is insufficient. Experiment 2 compared the accuracy of six supervised and machine learning classification algorithms to determine which performed best with this complex data set. The Random Forest classification algorithm produced the highest accuracies and was therefore used in Experiments 3 and 4. Experiment 3 assessed the benefits of using combined SAR-optical imagery over single-sensor imagery for land cover classifications on four separate dates. The fused imagery produced consistently higher overall accuracies. The 29 May 2014 fused data produced the best accuracy of 69.8%. The fused classifications had more consistent results over the four dates than the single-sensor imagery, which suffered lower accuracies, especially for imagery acquired later in the season. In Experiment 4, the use of multi-temporal, dual-sensor data for classification was evaluated. Feature selection was used to reduce the data set from 638 potential training features to 50, which produced the best accuracy of 74.1% in comparison to 71.9% using all of the features. This result validated the use of multi-temporal data over single-date data for land cover classifications. It also validated the use of feature selection to successfully inform data reduction without compromising the accuracy of the final product. Multi-temporal and dual-sensor data shows potential for mapping land cover in a tropical, mountainous region that would otherwise be challenging to map using single-sensor data. However, accuracies Stellenbosch University https://scholar.sun.ac.za iv generally remained lower than would allow for transferability and replication of the current methodology. Classification algorithm optimisation, supervised segmentation and improved training data should be considered to improve these results. / AFRIKAANSE OPSOMMING: Beeld-klassifikasie word al ‘n geruime tyd in aardwaarneming gebruik en word gedryf deur die behoefte aan akkurate kaarte om konseptuele en voorspellende modelle van aard-stelsel prosesse te ontwikkel. Sintetiese apertuur radar (SAR) beelde word ook meer dikwels in landdekking klassifikasie gebruik as gevolg van die aanvullende waarde daarvan met optiese data. Daar is dus 'n groeiende behoefte aan betroubare, akkurate metodes vir die gesamentlike gebruik van SAR en optiese data in landdekking klassifikasies. Die kombinasie van datastelle bring egter ‘n onvermydelike verhoging in data dimensionaliteit mee, en hierdie groot, komplekse datastelle is moeilik om te hanteer. Dus is dit belangrik om die voordele en beperkings van die gebruik van multi-temporale, dubbel-sensor data vir toepassings soos landdekking-klassifikasie te evalueer. Die waarde van gekombineerde (versmelte) RADARSAT-2 en SPOT-5 beelde word in hierdie tesis deur middel van vier eksperimente geevalueer. In Eksperiment 1 is die gebruik van kenmerk seleksie vir dimensionaliteit-vermindering toegepas. Die ranglys van belangrike kenmerke vir beide enkel-sensor en 'n dubbel-sensor data is beoordeel vir vier datums wat oor 'n tydperk van 6 maande strek. Die gemiddelde tekstuur kenmerke uit die optiese lae is konsekwent hoog oor alle datums geplaas. In die twee later datums (29 Mei en 9 Augustus 2014) was die SAR kenmerke meer algemeen, wat dui op die aanvullende aard van SAR en optiese data. SAR data dus gebruik kan word om klasse te onderskei wanneer optiese beelde onvoldoende daarvoor is. Eksperiment 2 het die akkuraatheid van ses gerigte en masjien-leer klassifikasie algoritmes vergelyk om te bepaal watter die beste met hierdie komplekse datastel presteer. Die random gorest klassifikasie algoritme het die hoogste akkuraatheid bereik en is dus in Eksperimente 3 en 4 gebruik. Eksperiment 3 het die voordele van gekombineerde SAR-optiese beelde oor enkel-sensor beelde vir landdekking klassifikasies op vier afsonderlike datums beoordeel. Die versmelte beelde het konsekwent hoër algehele akkuraathede as enkel-sensor beelde gelewer. Die 29 Mei 2014 data het die hoogste akkuraatheid van 69,8% bereik. Die versmelte klassifikasies het ook meer konsekwente resultate oor die vier datums gelewer en die enkel-sensor beelde het tot laer akkuraathede gelei, veral vir die later datums. In Eksperiment 4 is die gebruik van multi-temporale, dubbel-sensor data vir klassifikasie ge-evalueer. Kenmerkseleksie is gebruik om die data stel van 638 potensiële kenmerke na 50 te verminder, wat die beste akkuraatheid van 74,1% gelewer het. Hierdie resultaat bevestig die belangrikheid van multi-temporale data vir grond dekking klassifikasies. Dit bekragtig ook die gebruik van kenmerkseleksie om data vermindering suksesvol te rig sonder om die akkuraatheid van die finale produk te belemmer. Stellenbosch University https://scholar.sun.ac.za vi Multi-temporale en dubbel-sensor data toon potensiaal vir die kartering van landdekking in 'n tropiese, bergagtige streek wat andersins uitdagend sou wees om te karteer met behulp van enkel-sensor data. Oor die algemeen het akkuraathede egter te laag gebly om vir oordraagbaarheid en herhaling van die huidige metode toe te laat. Klassifikasie algoritme optimalisering, gerigte segmentering en verbeterde opleiding data moet oorweeg word om hierdie resultate te verbeter.

abstract: For synthetic aperture radar (SAR) image formation processing, the chirp scaling algorithm (CSA) has gained considerable attention mainly because of its excellent target focusing ability, optimized processing steps, and ease of implementation. In particular, unlike the range Doppler and range migration algorithms, the CSA is easy to implement since it does not require interpolation, and it can be used on both stripmap and spotlight SAR systems. Another transform that can be used to enhance the processing of SAR image formation is the fractional Fourier transform (FRFT). This transform has been recently introduced to the signal processing community, and it has shown many promising applications in the realm of SAR signal processing, specifically because of its close association to the Wigner distribution and ambiguity function. The objective of this work is to improve the application of the FRFT in order to enhance the implementation of the CSA for SAR processing. This will be achieved by processing real phase-history data from the RADARSAT-1 satellite, a multi-mode SAR platform operating in the C-band, providing imagery with resolution between 8 and 100 meters at incidence angles of 10 through 59 degrees. The phase-history data will be processed into imagery using the conventional chirp scaling algorithm. The results will then be compared using a new implementation of the CSA based on the use of the FRFT, combined with traditional SAR focusing techniques, to enhance the algorithm's focusing ability, thereby increasing the peak-to-sidelobe ratio of the focused targets. The FRFT can also be used to provide focusing enhancements at extended ranges. / Dissertation/Thesis / M.S. Electrical Engineering 2011

This thesis evaluates the potential for using line of sight returns and return signals from underneath a forest canopy using X-band, airborne synthetic aperture radar (SAR) tomography. Approximately 30% of the Earth’s land surface is covered by vegetation, therefore global digital elevation models (DEMs) contain a signal from the forest canopy and not the ground. By uncovering new techniques to find the ground signals, using data collected from airborne platforms as verification, such procedures could be applied to currently operational and future X-band, spaceborne systems with the aim of resolving much of the vegetation bias on an international scale. Data from three sources is presented; data collected from Selex ES’s SAR systems, the GOTCHA dataset and simulated data. Before carrying out tomography it is shown that SAR interferometry (InSAR) can successfully be applied to X-band, helicopter data. A scatterer defined as a candidate persistent scatterer (CPS) is introduced, where the pixels are stable and coherent over a matter of days. An algorithm for selecting CPSs is developed by exploiting sparsity and a novel choice of hard thresholding operator. Using simulated forestry and SAR information the effects of changing input parameters on the outcome of the tomographic profile is analysed. What is found in this study is that model simulations demonstrate that ground points can be detected if the platform motion is relatively stable and that temporal decorrelation over the forest volume is kept to a minimal. An understory can confuse the tomographic profile since less line of sight observations can be made. By combining line of sight observations alongside new tomography techniques on high resolution SAR data this thesis shows it is possible to detect ground scatterers, even at X-band.

Synthetic aperture radar (SAR) was first invented in the early 1950s as the remote surveillance instruments to produce high resolution 2D images of the illuminated scene with weather-independent, day-or-night performance. Compared to the Real Aperture Radar (RAR), SAR is synthesising a large virtual aperture by moving a small antenna along the platform path. Typical SAR imaging systems are designed with the basic assumption of a static scene, and moving targets are widely known to induce displacements and defocusing in the formed images. While the capabilities of detection, states estimation and imaging for moving targets with SAR are highly desired in both civilian and military applications, the Ground Moving Target Indication (GMTI) techniques can be integrated into SAR systems to realise these challenging missions. The state-of-the- art SAR-based GMTI is often associated with multi-channel systems to improve the detection capabilities compared to the single-channel ones. Motivated by the fact that the SAR imaging is essentially solving an optimisation problem, we investigate the practicality to reformulate the GMTI process into the optimisation form. Furthermore, the moving target sparsities and underlying similarities between the conventional GMTI processing and sparse reconstruction algorithms drive us to consider the compressed sensing theory in SAR/GMTI applications. This thesis aims to establish an end-to-end SAR/GMTI processing framework regularised by target sparsities based on multi-channel SAR models. We have explained the mathematical model of the SAR system and its key properties in details. The common GMTI mechanism and basics of the compressed sensing theory are also introduced in this thesis. The practical implementation of the proposed framework is provided in this work. The developed model is capable of realising various SAR/GMTI tasks including SAR image formation, moving target detection, target state estimation and moving target imaging. We also consider two essential components, i.e. the data pre-processing and elevation map, in this work. The effectiveness of the proposed framework is demonstrated through both simulations and real data. Given that our focus in this thesis is on the development of a complete sparsity-aided SAR/GMTI framework, the contributions of this thesis can be summarised as follows. First, the effects of SAR channel balancing techniques and elevation information in SAR/GMTI applications are analysed in details. We have adapted these essential components to the developed framework for data pre-processing, system specification estimation and better SAR/GMTI accuracies. Although the purpose is on enhancing the proposed sparsity-based SAR/GMTI framework, the exploitation of the DEM in other SAR/GMTI algorithms may be of independent interest. Secondly, we have designed a novel sparsity-aided framework which integrates the SAR/GMTI missions, i.e. SAR imaging, moving target and background decomposition, and target state estimation, into optimisation problems. A practical implementation of the proposed framework with a two stage process and theoretically/experimentally proven algorithms are proposed in this work. The key novelty on utilising optimisations and target sparsities is explained in details. Finally, a practical algorithm for moving target imaging and state estimation is developed to accurately estimate the full target parameters and form target images with relocation and refocusing capabilities. Compared to the previous processing steps for practical applications, the designed algorithm consistently relies on the exploitation of target sparsities which forms the final processing stage of the whole pipeline. All the developed components contribute coherently to establish a complete sparsity driven SAR/GMTI processing framework.