Humans perceive the world in three dimensions, but many sensing capabilities only display two-dimensional information to users by way of images. In this work we develop two novel reconstruction techniques utilizing synthetic aperture radar (SAR) data in three dimensions given sparse amounts of available data. We additionally leverage a hybrid joint-sparsity and sparsity approach to remove a-priori influences on the environment and instead explore general imaging properties in our reconstructions. We evaluate the required sampling rates for our techniques and a thorough analysis of the accuracy of our methods. The results presented in this thesis suggest a solution to sparse three-dimensional object reconstruction that effectively uses a substantially less amount of phase history data (PHD) while still extracting critical features off an object of interest.

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Yes / A metasurface based decoupling structure that is composed of a square-wave slot pattern with exaggerated corners that is implemented on a rectangular microstrip provides high-isolation between adjacent patch antennas for Synthetic Aperture Radar (SAR) and Multi-Input-Multi-Output (MIMO) systems. The proposed 1×2 symmetric array antenna integrated with the proposed decoupling isolation structure is designed to operate at ISM bands of X, Ku, K, and Ka. With the proposed mutual coupling suppression technique (i) the average isolation in the respective ISM bands listed above is 7 dB, 10 dB, 5 dB, and 10 dB; and (ii) edge-to-edge gap between adjacent radiation elements is reduced to 10 mm (0.28λ). The average antenna gain improvement with the metasurface isolator is 2 dBi. / H2020-MSCA-ITN-2016 SECRET-722424 and the financial support from the UK Engineering and Physical Sciences Research Council (EPSRC) under grant EP/E0/22936/1

Land cover classification using Synthetic Aperture Radar (SAR) data has been a topic of great interest in recent literature. Food commodities output prediction through crop identification, environmental monitoring, and forest regrowth tracking are some of the many problems that can be aided by land cover classification methods. The need for fast and automated classification methods is apparent in a variety of applications involving vast amounts of SAR data. One fundamental step in any supervised learning classification algorithm is the selection and/or extraction of features present in the dataset to be used for class discrimination. A popular method that has been proposed for feature extraction from polarimetric data is to decompose the data into the underlying scattering mechanisms. In this research, the Freeman and Durden scattering model is applied to ALOS PALSAR fully polarimetric data for feature extraction. Efficient methods for solving the complex system of equations present in the scattering model are developed and compared. Using the features from the Freeman and Durden work, the classification capability of the model is assessed on amazon rainforest land cover types using a supervised Support Vector Machine (SVM) classification algorithm. The quantity of land cover types that can be discriminated using the model is also determined. Additionally, the performance of the median as a robust estimator in noisy environments for multi-pixel windowing is also characterized. / Master of Science / Land type classification using Radar data has been a topic of great interest in recent literature. Food commodities output prediction through crop identification, environmental monitoring, and forest regrowth tracking are some of the many problems that can be aided by land cover classification methods. The need for fast and automated classification methods is apparent in a variety of applications involving vast amounts of Radar data. One fundamental step in any classification algorithm is the selection and/or extraction of discriminating features present in the dataset to be used for class discrimination. A popular method that has been proposed for feature extraction from polarized Radar data is to decompose the data into the underlying scatter components. In this research, a scattering model is applied to real world data for feature extraction. Efficient methods for solving the complex system of equations present in the scattering model are developed and compared. Using the features from the scattering model, the classification capability of the model is assessed on amazon rainforest land types using a Support Vector Machine (SVM) classification algorithm. The quantity of land cover types that can be discriminated using the model is also determined and compared using different estimators.

天津是中國遭受地面沉降最嚴重的城市之一。由於經濟與城市化的快速發展，新的沉降中心陸續出現在天津的郊區城鎮。本文結合L-和X-波段合成孔徑雷達(Synthetic Aperture Radar, SAR)資料，利用雷達干涉測量(SAR Interferometry, InSAR)時間序列分析，旨在加強天津郊區的沉降監測能力。先進的基於SAR資料的遙感技術，永久散射體干涉測量(Permanent Scatterers, PS)技術被證明是一種有效的，大範圍的，低成本的沉降監測手段。 / 工作在X波段(波長為3.1cm)的TerraSAR (TSX)衛星可以提供新一代具有高解析度(1米)和短重放週期(11天)的SAR資料，從而能夠更快的獲取適用於干涉的時間序列的資料，並且適用於單個建築物的沉降觀測。然而，利用X-波段在森林或植被覆蓋區域並不能得到有效資訊。ALOS衛星的SAR感測器工作在L波段，由於波長更長(波長為23cm)，穿透力更強，所以在植被覆蓋區域也具有良好的相干性。但是ALOS衛星的SAR資料解析度更低(7米)，重放週期更長(46天)。從這兩個波段的資料特徵來看，他們可以被認為是互補的。所以，結合這兩個波段的資料可以增強沉降監測的能力和提供更為可靠的結果。儘管ALOS衛星於2011年4月22日停止了工作，我們的研究結果仍然可以為結合不同波段的SAR資料進行沉降監測提供普遍適用的結論，並為以後的研究工作提供參考。 / 在研究中，我們提出了結合L和X波段的InSAR時間序列分析策略。此策略不僅可以作為X波段資料最優化獲取方案，而且可以成為快速，高精度，低成本，多級，大範圍監測策略。 / 其次，我們基於多時序SAR資料，利用PS和准PS(Quasi-PS, QPS)技術進行了L波段與X波段的沉降監測能力探尋。L波段和X波段的時間序列分析所得到的沉降模式有很好的吻合性，都監測出三個主要的沉降中心，其中包括一個新近發現的沉降中心位於南河鎮。 / X波段的PS分析結果顯示出高密度的PS點，證實了它可以用於同時監測星狀分佈的多個城鎮。結果也表明了高解析度TSX資料可以監測到線狀地物如鐵路，高速公路以及電力線的細節資訊和沉降資訊，這些可以成為高解析度PS技術在中國的重要應用。 / 除此之外，我們利用水準資料驗證了L和X波段的處理結果，並且對地面沉降的過程進行了研究。由於水準資料和PS監測結果在時間和空間維上的採樣差別很大，所以我們對這兩者比較所具有的不確定性進行了詳細分析。結果表明了這兩種監測資料具有很好的一致性。 / 最後，我們發現在天津抽取地下水是引起地面沉降的一個主要原因。根據PS結果和地質資料，我們發現地質因素可能是另一個用於解釋沉降中心位置和形狀的原因。 / The aim of this dissertation is to enhance the capability of monitoring subsidence in Tianjin suburbs by combining L- and X-band Synthetic Aperture Radar (SAR) data with Interferometry (InSAR) time series analysis. Tianjin is located in one of the major subsidence regions in China and several new subsiding centers have been found in the suburbs of Tianjin. Advanced remote sensing technique, Permanent Scatterers (PS) based on SAR data has been found to be a feasible way to detect and monitor wide area ground subsidence at a low cost. / TerraSAR X-band (TSX) of short wavelength (3.1 cm) provides new generation SAR data with high spatial resolution of 1 m and short revisit period of 11 days. It maintains the capability to fast build up interferometric stack, and to measure the subsidence of individual features, while almost no information can be detected with X-band in the forested and vegetated areas. ALOS L-band signal of longer wavelength (23cm) penetrates deeper into the vegetation cover and depicts higher coherence over non-urban areas, while the spatial resolution is relatively lower (7m) and revisit time is longer (46 days). The characteristics of these two bands can be regarded as complementary. Combining L- and X-band can enhance abilities of subsidence monitoring and provide more reliable results. Although ALOS died on April 22, 2011, this research work will provide general answers for combining different bands of SAR data to monitor subsidence, and give suggestions for future research work. / In this research work, we have developed the strategy of combining L- and X-band with InSAR time series analysis. This strategy can not only be an optimized X-band acquisition plan, but also be a multi-level wide area monitoring strategy of subsidence with fast extraction, high precision and low cost. / Moreover, with multi-temporal SAR data, we also investigate monitoring abilities of L- and X-band by exploring PS and Quasi-PS (QPS) techniques. The subsidence patterns derived from L- and X-band InSAR time series analysis are observed to have a good agreement. Three severe land subsidence zones were detected, containing one newly discovered subsiding center located in Nanhe Town. / The X-band PS analysis shows high density of PS points and confirms its strong ability for simultaneously monitoring subsidence over star-like-distributed multiple towns. The results also demonstrate that linear constructions such as railways, highways and power lines can be detected in detail with high resolution TSX SAR data and indicates the deformation monitoring capability for large-scale man-made linear features which is a key application in China. / Furthermore, L- and X-band results were independently validated with leveling data and ground motion processes were studied. The uncertainties were comprehensively analyzed between PS results and ground leveling data, whose densities are very different in both spatial and temporal domains. The overall results show a good agreement with each other. / Finally, we find that underground water extraction is one of the major reasons for ground subsidence in Tianjin. In addition, with the integrated analysis of the PS results and the geological data, we found that lithological characteristics may be another important reason to explain location and shape of the subsiding centers. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Luo, Qingli. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 103-112). / Abstract also in Chinese. / Abstract --- p.I / TABLE OF CONTENT --- p.VI / List of Figures --- p.VIII / List of Tables --- p.XI / List of abbreviations --- p.XII / ACKNOWLEDGEMENT --- p.XIV / Chapter 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Thesis contributions --- p.6 / Chapter 1.2 --- Thesis structure --- p.7 / Chapter 2 --- BACKGROUND --- p.9 / Chapter 2.1 --- Synthetic Aperture Radar (SAR) --- p.9 / Chapter 2.1.1 --- SAR imaging geometry --- p.9 / Chapter 2.1.2 --- SAR satellites --- p.10 / Chapter 2.2 --- Synthetic Aperture Radar Interferometry (InSAR) --- p.13 / Chapter 2.2.1 --- Introduction --- p.13 / Chapter 2.2.2 --- Principles of InSAR --- p.13 / Chapter 2.3 --- Differential Synthetic Aperture Radar Interferometry (D-InSAR) --- p.18 / Chapter 2.3.1 --- D-InSAR principle --- p.18 / Chapter 2.3.2 --- The advantages and Limits of interferometric measurements --- p.21 / Chapter 2.4.3 --- The development of PS technique --- p.22 / Chapter 2.4 --- Persistent Scatterers Interferometry (PSI) --- p.24 / Chapter 2.4.1 --- Permanent Scatterers (PS) Technique and Advantages --- p.24 / Chapter 2.4.2 --- Principle of PS technique --- p.26 / Chapter 2.5 --- QPS (Quasi-PS) Interferometry --- p.28 / Chapter 3 --- MULTI IMAGES INSAR ANALYSIS OF TIANJIN --- p.31 / Chapter 3.1 --- Introduction --- p.32 / Chapter 3.2 --- Study area and SAR data --- p.34 / Chapter 3.3 --- X-band optimized acquisition planning combing with L-band --- p.38 / Chapter 3.3.1 --- The strategy --- p.38 / Chapter 3.3.2 --- Experimental results and analyzes --- p.40 / Chapter 3.4 --- Estimating deformation maps with L- and X-band --- p.45 / Chapter 3.4.1 --- Monitoring subsidence over multiple towns and large man-made linear features with X-band --- p.45 / Chapter 3.4.2 --- The L-band QPS Results --- p.56 / Chapter 3.5 --- Conclusions --- p.58 / Chapter 4 --- VALIDATION AND INTERPRETAION --- p.61 / Chapter 4.1 --- Introduction --- p.61 / Chapter 4.2 --- Validation --- p.61 / Chapter 4.2.1 --- Leveling data --- p.61 / Chapter 4.2.2 --- Uncertainties analysis --- p.64 / Chapter 4.2.3 --- Average velocity comparison --- p.66 / Chapter 4.2.4 --- Annual displacement comparison --- p.68 / Chapter 4.2.5 --- Deformation time series: InSAR results and leveling --- p.70 / Chapter 4.2.6 --- Average velocity map comparison between InSAR results and leveling --- p.71 / Chapter 4.2.7 --- Displacement comparison between InSAR results and GNSS data --- p.73 / Chapter 4.2.8 --- Average velocity comparison between ALOS results and leveling --- p.73 / Chapter 4.3 --- Geological Interpretation --- p.74 / Chapter 4.4 --- Field survey --- p.77 / Chapter 4.5 --- QPS points analysis with aerophotograph --- p.81 / Chapter 4.6 --- Conclusions --- p.84 / Chapter 5 --- VALIDATION ALONG RAILWAY --- p.87 / Chapter 5.1 --- Introduction --- p.87 / Chapter 5.2 --- Study area --- p.87 / Chapter 5.3 --- The validation plan --- p.87 / Chapter 5.4 --- Validation with leveling data --- p.89 / Chapter 5.4.1 --- Leveling data --- p.89 / Chapter 5.4.2 --- The average subsidence rate comparison --- p.91 / Chapter 5.4.3 --- The displacement comparison --- p.95 / Chapter 5.5 --- Conclusions --- p.97 / Chapter 6 --- SUMMARY --- p.98 / The Publications --- p.102 / REFERENCES --- p.103

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International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / In this paper, we discuss a Block Adaptive Vector Quantization(BAVQ) Algorithm for Synthetic Aperture Radar(SAR). And we discuss a realization method of BAVQ algorithm for SAR raw data compressing in digital signal processor. Using the algorithm and the digital signal processor, we have compressed the SIR_C/X_SAR data.

Synthetic aperture radar is an important tool in a wide range of civilian and military imaging applications. This is primarily due to its ability to image in all weather conditions, during both the day and the night, unlike optical imaging systems. A synthetic aperture radar system contains a step which is not present in an optical imaging system, this is image formation. This is required because the acquired data from the radar sensor does not directly correspond to the image. Instead, to form an image, the system must solve an inverse problem. In conventional scenarios, this inverse problem is relatively straight forward and a matched lter based algorithm produces an image of suitable image quality. However, there are a number of interesting scenarios where this is not the case. Scenarios where standard image formation algorithms are unsuitable include systems with data undersampling, errors in the system observation model and data that is corrupted by radio frequency interference. Image formation in these scenarios will form the topics of this thesis and a number of iterative algorithms are proposed to achieve image formation. The motivation for these proposed algorithms is primarily from the eld of compressed sensing, which considers the recovery of signals with a low-dimensional structure. The rst contribution of this thesis is the development of fast algorithms for the system observation model and its adjoint. These algorithms are required by large-scale gradient based iterative algorithms for image formation. The proposed algorithms are based on existing fast back-projection algorithms, however, a new decimation strategy is proposed which is more suitable for some applications. The second contribution is the development of a framework for iterative near- eld image formation, which uses the proposed fast algorithms. It is shown that the framework can be used, in some scenarios, to improve the visual quality of images formed from fully sampled data and undersampled data, when compared to images formed using matched lter based algorithms. The third contribution concerns errors in the system observation model. Algorithms that correct these errors are commonly referred to as autofocus algorithms. It is shown that conventional autofocus algorithms, which work as a post-processor on the formed image, are unsuitable for undersampled data. Instead an autofocus algorithm is proposed which corrects errors within the iterative image formation procedure. The proposed algorithm is provably stable and convergent with a faster convergence rate than previous approaches. The nal contribution is an algorithm for ultra-wideband synthetic aperture radar image formation. Due to the large spectrum over which the ultra-wideband signal is transmitted, there is likely to be many other users operating within the same spectrum. These users can produce signi cant radio frequency interference which will corrupt the received data. The proposed algorithm uses knowledge of the RFI spectrum to minimise the e ect of the RFI on the formed image.

The UK has an abundant offshore wind resource with offshore wind farming set to grow rapidly over the coming years. Optimisation of energy production is of the utmost importance and accurate estimates of wind speed distributions are critical for the planning process. Synthetic aperture radar (SAR) data can provide synoptic, wide area wind field estimates at resolutions of a few kilometres and has great potential for wind resource assessment. This thesis addresses the key challenges for the operational implementation of SAR in this context; namely the accuracy of SAR wind retrievals and the ability of SAR to characterise the mean wind speed and wind power density. We consider the main stages of SAR wind retrieval; the retrieval algorithm; sources of a priori information; the optimal configuration of the retrieval system; and the challenges for and accuracy of SAR wind resource estimation. This study was conducted for the eastern Irish Sea in the UK, a region undergoing significant offshore wind energy development. A new wind retrieval algorithm was developed that implements a maximum a posterior probability (MAP) method drawn from Bayesian statistics. MAP was demonstrated to be less sensitive to input errors than the standard direction-based wind speed algorithm (DWSA) and provides a simple retrieval quality check via the error reduction ratio. Retrieval accuracy is strongly influenced by the quality of a priori information. The accuracy of two operationally viable a priori sources, mesoscale numerical weather prediction (NWP) data and WISAR image directions, was evaluated by comparison against in-situ wind observations and WERA coastal data. Results show that NWP wind speeds produce good wind speed and direction estimates with standard deviations of ¬±2 ms-1 and ±16o respectively. WISAR directions were less accurate producing standard deviations ranging from ±20o to ±29o, but were preferable when strong differences between NWP timesteps were observed. The accuracy of SAR wind retrievals was evaluated by comparison against in-situ wind observations. The MAP algorithm was found to provide modest improvements in retrieval accuracy over DWSA. Highest quality retrievals achieved using the CMOD5 forward model, producing wind speeds with a RMSE of 1.83 ms-1. Regarding the ability of SAR to estimate offshore wind resources, dataset density was found to be a controlling parameter. With 103 scenes available mean wind speeds were well characterised by comparison against in-situ observations and Wind Atlas results, while wind power density showed considerable errors. The accuracy of wind speed maps was further improved by accounting for wind direction and fetch effects upon the SAR wind distribution. A key strength of the SAR wind fields is their ability to identify the effect of mesoscale structures upon the surface wind field with atmospheric gravity waves observed in 30% of the images. These structures are shown to introduce wind speed fluctuations of up to ±2 ms-1 at scales of 5 to 10 km and may have significant implications for wind power prediction. These findings show that SAR may provide an important source of wide area wind speed observations as a complement to existing wind resource estimation techniques. SAR may be of particular use in coastal areas where complex wind fields are observed.

Optimization algorithms were developed for use with the Adaptive Joint Time-Frequency (AJFT) algorithm to reduce Inverse Synthetic Aperture Radar (ISAR) image blurring caused by higher-order target motion. A specific optimization was then applied to 3D motion detection. Evolutionary search methods based on the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm were designed to rapidly traverse the solution space in order to find the parameters that would bring the ISAR image into focus in the cross-range. 3D motion detection was achieved by using the AJTF PSO to extract the phases of 3 different point scatterers in the target data and measuring their linearity when compared to an ideal phase for the imaging interval under investigation. The algorithms were tested against both simulated and real ISAR data sets.