Approximations made in the traditional signal model for synthetic aperture radar (SAR) processing cause defocusing of the radar images when the system operates under conditions where the approximations lose validity. This dissertation investigates a number of these approximations and presents algorithmic improvements based on generalizations of the approxmations of the SAR signal model. These improvements result in better focused imagery from SAR systems with varied designs and parameters. Among the advancements presented is the development of a generalized chirp-scaling algorithm and a generalized frequency scaling algorithm to address the problems caused by approximations based on a Taylor series expansion of the SAR signal for both pulsed SAR and linear frequency modulated continuous wave (LFM-CW) SAR systems. These generalized algorithms extend the ability of frequency-domain algorithms to process SAR data from systems with a low frequency, a wide beamwidth, and a large bandwidth. Image formation algorithms are developed that account for the continuous platform motion and compensate for translational position errors due to the continuous non-ideal motion of real-world LFM-CW SAR systems, including a backprojection algorithm that does not rely upon the traditional stop-and-go approximation for platform motion.

Synthetic aperture radar (SAR) is a type of radar capable of high-resolution coherent imaging. In order to produce coherent imagery from raw SAR data, an image formation algorithm is employed. The various image formation algorithms have strengths and weaknesses. As this work shows, time-domain backprojection is one algorithm whose strengths are particularly well-suited to use at low-altitudes. This work presents novel research in three areas regarding time-domain backprojection. The first key contribution of this work is a detailed analysis of SAR time-domain backprojection. The work derives a general form of backprojection from first principles. It characterizes the sensitivities of backprojection to the various inputs as well as error sources and performance characteristics. This work then shows what situations are particularly well-suited to use of the backprojection algorithm, namely regimes with turbulent motion and wide variation in incidence angle across the range swath (e.g., low-altitude, airborne SAR).The second contribution of this work is an analysis of geometric signal correlation for multi-static, sometimes termed multiple-input and multiple-output (MIMO), imaging. Multi-static imaging involves forming multiple images using different combinations of transmitters and receivers. Geometric correlation is a measure of how alike observations of a target are from different aspect angles. This work provides a novel model for geometric correlation which may be used to determine the degree to which multi-static images are correlated. This in turn determines their applicable use: operating in the highly correlated regime is desirable for coherent processing whereas operating in a lower-correlation regime is desirable for obtaining independent looks. The final contribution of this work is a novel algorithm for interferometry based on backprojected data. Because of the way backprojected images are formed, they are less suited to traditional interferometric methods. This work derives backprojection interferometry and compares it to the traditional method of interferometry. The sensitivity and performance of backprojection interferometry are shown, as well as where backprojection interferometry offers superior results. This work finds that backprojection interferometry performs better with longer interferometric baseline lengths or systems with large measurement error in the baseline length or angle (e.g., low-altitude, airborne SAR).

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.