Recent development in electronics for mobile devices has led to the decrease in sizes and cost of autonomous complex embedded systems such as satellites. It is now possible to build a satellite quicker and only for a fraction of previous costs by using Commercial Off The Shelf (COTS) components. Yet, there are some obstacles that need to be overcome before a successful small satellite can be designed. Among these are the radiation environment, thermal issues, the overall system complexity and tight schedules. This thesis addresses these issues and proposes an overall approach for designing small satellites’ electronics. This approach can be summarised in 6 recommendations: Keep it simple Use fast hardware iterations Do not use space grade components Use a single string design on the system level (no redundancy) Design with limited trust in the software Use simple, accessible and easy updatable documentation With respect to those recommendations an on board data processing system, the Processing Board, has been designed for the ICEYE-1 satellite. The ICEYE-1 satellite is a fully commercial Synthetic Aperture Radar (SAR) satellite that will be launched in December 2017. The designed board has been manufactured and verified during airborne test campaigns. / Nya elektronikutvecklingar för mobiltelefoner har lett till en minskning av storlek och kostnader för andra autonoma komplexa inbyggda system som t.ex. satelliter. Så kallade småsatelliter kan numera byggas snabbare och för endast en bråkdel av tidigare kostnader med hjälp av Commercial Off The Shelf (COTS) komponenter. Det finns dock vissa hinder som måste övervinnas om man vill designa en pålitligt fungerande småsatellit. Till dessa kan räknas strålningsmiljön, väl fungerande värmeledning, det totala systemets komplexitet samt snäva tidtabeller. Detta examensarbete behandlar dessa frågor och föreslår en övergripande strategi för att designa elektronik för småsatelliter. Detta tillvägagångssätt kan sammanfattas i 6 rekommendationer: Håll det enkelt Implementera snabba hårdvaruiterationer Använd inte rymdklassade komponenter Använd ingen redundans på systemnivå Designa med en begränsad tilltro på mjukvaran Dokumentera på ett enkelt, tillgängligt och lätt uppdateringsbart sätt Dessa rekommendationer har använts till att utveckla ett databehandlingssystem, kallat "Processing Board", till småsatelliten ICEYE-1. ICEYE-1 är en kommersiell Synthetic Aperture Radar (SAR) satellit som kommer att skjutas i omloppsbana i december 2017. Databehandlingssystemet i fråga har utvecklats och verifierats i samband med flygplansburna testkampanjer.

Field measurements were combined with synthetic aperture radar (SAR) images to evaluate the use of C-band multi-polarized radar remote sensing for estimating plant parameters (plant height, fresh biomass, dry biomass and vegetation water content) of wetland vegetation, and mapping the dynamics of water bodies, wetlands (natural wetlands and rice paddies) and flooding extents in the Poyang Lake region. The capacity of L-band SAR in land cover mapping was also investigated by integrating with optical imagery. / Hydrological patterns in Poyang Lake are the dominant factor controlling the spatial and temporal variations of wetland species in Poyang Lake. Water levels in this region are primarily governed by five rivers (Ganjiang river, Xiushui river, Raohe river, Fuhe river, and Xinjiang river). Its northern region is also influenced by the backflow from Yangtze River. The above-ground total biomass increased steadily from March following the hydrological cycle. Wetland species colonizing at different altitudes were gradually flooded from late spring to summer. Carex spp. died during flooding periods and started another growth cycle in autumn after flooding receded. Canopy volume dominates the radar backscattering mechanism in Carex spp. wetlands during their growth period, but the temporal variation of radar backscatter from these wetlands is mainly influenced by flooding. Tall wetland species (Miscanthus sacchariflorus, Phragmites communis Trin., and others) still emerged above water surfaces during flooding peaks and started to senesce in autumn. Surface backscattering mechanism is dominant during the early growing stage and the senescent period of tall vegetation. Plant canopy variation controlled the temporal dynamics of radar backscatters from Phragmites communis Min. Radar backscattering mechanisms from Miscanthus sacchariflorus wetlands were more complicated during the flooding periods. The variations of ground water depth and plant structure of Miscanthus sacchariflorus during its growth period result in over 10 dB spatial and temporal variation in ASAR backscatter in HH- and HV-polarization. / Temporal profiles of C-band multi-polarized backscatter coefficients for individual land cover types over the period of December 2004 to November 2005 were studied and described in the context of the ecology and seasonal dynamics of biophysical parameters of individual land cover types. A knowledge-based hierarchical land cover mapping method was developed to quantify the dynamics of paddy rice, natural wetlands and floods using the time series of HH- and HV-backscatters. The specific phenological and ecological characteristics of wetlands including paddy rice are the most important data in mapping their spatial and temporal patterns. The classification accuracy is over 90% for water bodies, rice paddies and Carex spp. wetlands, but it is not high for tall wetlands (68%). A decision tree approach was adopted to evaluate the capacity of L-band SAR in land cover mapping by combining with optical imagery. Classification errors were mainly induced by the mixed spectrum between and covers, and lack of independent training data and validation data also caused uncertainty in the results. / The relationship of canopy height with ASAR backscattering coefficient is the most significant among the influencing factors (plant height, fresh biomass, dry biomass, vegetation water content) on radar backscattering mechanism (R2=0.9 for HH-polarization and R2=0.59 for HV-polarization) from Phragmites cummunis Trin. HH- and HV-backscatters are more sensitive to the variation of dry biomass (R2=0.76 for HH and R2=0.56 for HV) than to that of fresh biomass (R 2=0.07 for HV and R2=0.42 for HH). Plant water content plays a negative role and attenuates the backscattering signals in both polarizations. For Phragmites communis Trin. with tall stalks (over 2m) and long, blade-like leaves, HH-polarization is more sensitive to vegetation parameters than HV-polarization for C-band SAR signals. Similar to Phragmites communis Trin., ASAR backscattering coefficient in both polarizations is more sensitive to plant height and dry biomass of non-flooded Miscanthus sacchariflorus, and their regression coefficients (R2) are over 0.5 for HH-polarization and over 0.4 for HV-polarization. Plant water content has no evident effect on the variation of ASAR backscatter. HV-polarization is more sensitive to the variation of above-water canopy parameters than HH-polarization for flooded Miscanthus saccharifiorus. HH- and HV-polarized radar backscatters from Carex spp. wetlands increased significantly with the variation of plant height, fresh biomass and dry biomass, but they reach saturated when vegetation grows up to 30cm. Compared with those tall grass with stalks and long blade-like leaves, the correlation of fresh biomass with HV-polarization is more pronounced (R 2=0.78) than that with HH-polarization (R2=0.41) for Carex spp. Vegetation structure play a more important role in radar backscattering mechanism than plant water content for these three wetland species. / Sang, Huiyong. / "April 2008." / Adviser: Hui Lin. / Source: Dissertation Abstracts International, Volume: 70-03, Section: B, page: 1443. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (p. 149-159). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Synthetic aperture radar (SAR) is a remote sensing technology for imaging areas of the earth's surface. SAR has been successfully used for monitoring characteristics of the natural environment such as land cover type and tree density. With the advent of higher resolution sensors, it is now theoretically possible to extract information about individual structures such as buildings from SAR imagery. This information could be used for disaster response and security-related intelligence. SAR has an advantage over other remote sensing technologies for these applications because SAR data can be collected during the night and in rainy or cloudy conditions. This research presents a model-based method for extracting information about a building -- its height and roof slope -- from a single SAR image. Other methods require multiple images or ancillary data from specialized sensors, making them less practical. The model-based method uses simulation to match a hypothesized building to an observed SAR image. The degree to which a simulation matches the observed data is measured by mutual information. The success of this method depends on the accuracy of the simulation and on the reliability of the mutual information similarity measure. Electromagnetic theory was applied to relate a building's physical characteristics to the features present in a SAR image. This understanding was used to quantify the precision of building information contained in SAR data, and to identify the inputs needed for accurate simulation. A new SAR simulation technique was developed to meet the accuracy and efficiency requirements of model-based information extraction. Mutual information, a concept from information theory, has become a standard for measuring the similarity between medical images. Its performance in the context of matching a simulation image to a SAR image was evaluated in this research, and it was found to perform well under certain conditions. The factors that affect its performance, and the model-based method overall, were found to include the size of the building and its orientation. Further refinements that expand the range of operational conditions for the method would lead to a practical tool for collecting information about buildings using SAR technology. This research was performed using SAR data from MIT-Lincoln Laboratory.

The ASCE confers an overall D+ grade to American infrastructure, while the NAE lists the restoration and improvement of urban infrastructure as one of its grand engineering challenges for the 21st century, indicating that infrastructure renovation and development is a major challenge in the US. Furthermore, according to the UN World Urbanization Prospects, about 55% of the world's population lives in urban areas and this percentage is set to grow, especially in Africa and Asia. The growth of urban population poses challenges to the expansion of underground infrastructure, such as water, sewage, electricity and telecommunications. Localization and mapping of underground infrastructure are fundamental for infrastructure maintenance and development. Ground penetrating radar (GPR) is a remote sensing method capable of detecting subsurface assets that has been used in the localization and mapping of underground utilities. This thesis contributes improvements of GPR systems and imaging algorithms towards smarter infrastructure, specifically: Application of GPR imaging algorithm to improve GPR data readability and generate augmented reality (AR) content; Use of photogrammetric methods to improve GPR positioning for underground infrastructure localization and mapping.

Synthetic aperture radar (SAR) is a type of remote sensor that provides its own illumination and is capable of forming high resolution images of the reflectivity of a scene. The reflectivity of the scene that is measured is dependent on the choice of carrier frequency; different carrier frequencies will yield different images of the same scene. There are different modes for SAR sensors; two common modes are spotlight mode and stripmap mode. Furthermore, SAR sensors can either be continuously transmitting a signal, or they can transmit a pulse at some pulse repetition frequency (PRF). The work in this dissertation is for pulsed stripmap SAR sensors. The resolvable limit of closely spaced reflectors in range is determined by the bandwidth of the transmitted signal and the resolvable limit in azimuth is determined by the bandwidth of the induced azimuth signal, which is strongly dependent on the length of the physical antenna on the SAR sensor. The point-spread function (PSF) of a SAR system is determined by these resolvable limits and is limited by the physical attributes of the SAR sensor. The PSF of a SAR system can be defined in different ways. For example, it can be defined in terms of the SAR system including the image processing algorithm. By using this definition, the PSF is an algorithm-specific sinc-like function and produces the bright, star-like artifacts that are noticeable around strong reflectors in the focused image. The PSF can also be defined in terms of just the SAR system before any image processing algorithm is applied. This second definition of the PSF will be used in this dissertation. Using this definition, the bright, algorithm-specific, star-like artifacts will be denoted as the inter-pixel interference (IPI) of the algorithm. To be specific, the combined effect of the second definition of PSF and the algorithm-dependent IPI is a decomposition of the first definition of PSF. A new comprehensive forward model for stripmap SAR is derived in this dissertation. New image formation methods are derived in this dissertation that invert this forward model and it is shown that the IPI that corrupts traditionally processed stripmap SAR images can be removed. The removal of the IPI can increase the resolvability to the resolution limit, thus making image analysis much easier. SAR data is inherently corrupted by uncompensated phase errors. These phase errors lower the contrast of the image and corrupt the azimuth processing which inhibits proper focusing (to the point of the reconstructed image being unusable). If these phase errors are not compensated for, the images formed by system inversion are useless, as well. A model-based autofocus method is also derived in this dissertation that complements the forward model and corrects these phase errors before system inversion.

Estimation of biomass has high importance for economic, ecologic and climatic reasons due to the multiple ecosystem services offered by forested landscapes. Measurements that are taken in the field incur personal and economic costs. Nevertheless, biomass surveying based on remote sensing techniques offer efficiency thanks to covering large areas. The European Space Agency (ESA) Sentinel-1 satellite offers promising capabilities for above-ground biomass (AGB) estimation through synthetic aperture radar (SAR) based microwave remote sensing. In this study, experimental AGB estimations based on Sentinel-1 C-band data were produced over the Remingstorp estate (Västergötland County, Sweden) to analyze its performance over boreal productive forests. The obtained measurements were compared against reference values obtained by combining photogrammetric, aerial laser scanning (ALS) and field measurements. Thus, a reference high-resolution canopy height model (CHM) was produced from the difference between photogrammetric digital surface model (DSM) values and ALS digital terrain model (DTM) values. The comparison of CHM observations against diameter at breast height (DBH) field measurements revealed the existence of a vegetation height - vegetation volume relationship for the study species (Pinus Sylvestris and Picea Abbies), which allowed bole volume estimation based on vegetation height values. SAR-based AGB estimates were produced by defining statistical relationships between backscatter intensity and interferometric coherence measurements against reference CHM values. Additionally, evaluation of biomass estimation through interferometric (InSAR) height was possible by comparing against reference photogrammetric DSM. Backscatter signal saturation of C-band at low biomass volumes prevented quantification of biomass but permitted differentiation between forested and non-forested surfaces. Estimation of AGB through interferometric coherence was possible through modeling volumetric decorrelation, which on the contrary prevented biomass retrieval from InSAR height. Due to the given frequency properties at C-band, HV cross-polarized channel was used in all cases for better detection of the canopy layer. Image acquisition under stable conditions was a priority to avoid noise derived from variable dielectric properties, acquisition geometry effects and temporal decorrelation. Hence, image acquisitions under stable hydrometeorological conditions (i. e. stable frozen or dry) and for the lowest repeat-pass interval (i. e. 6-days) were prioritized.

In this thesis, Inverse Synthtetic Aperture Radar image reconstruction techniques, named as Range Doppler, Back Projection, Polar Formatting, Multiple Signal Classification (MUSIC) and Time Frequency techniques are analysed and compared using simulations. Time Frequency techniques investigated in this thesis are Short Time Fourier Transform, Wigner-Ville Distribution, Smoothed Wigner-Ville Distribution and Choi-Williams Distribution. First, some fundamental concepts of ISAR, such as resolution, range profile, time dependent Doppler frequency are given. A data simulator is designed and implemented for the purpose of providing configurable input to ISAR signal processing algorithms for a given ISAR target geometry. Estimation of target rotational velocity is explained with the help of three methods, namely Grid Search, WVD Slope and Radon Wigner-Hough Transform. Then, theoretical background of image formation algorithms is discussed. MATLAB simulations for each algorithm are implemented with several configurations in order to visualize and analyse the results. Finally, processing algorithms are compared to discuss the advantages and disadvantages.

In remote sensing, segmentation is a procedure of partitioning the domain of a remotely sensed dataset into meaningful regions which correspond to different land use and land cover (LULC) classes or part of them. So far, the remotely sensed data segmentation is still one of the most challenging problems addressed by the remote sensing community, partly because of the availability of remotely sensed data from diverse sensors of various platforms with very high spatial resolution (VHSR). Thus, there is a strong motivation to propose a sophisticated data representation that can capture the significant amount of details presented in a VHSR dataset and to search for a more powerful scheme suitable for multiple remotely sensed data segmentations. This thesis focuses on the development of a segmentation framework for multiple VHSR remotely sensed data. The emphases are on VHSR data model and segmentation strategy. Starting with the domain partition of a given remotely sensed dataset, a hierarchical data model characterizing the structures hidden in the dataset locally, regionally and globally is built by three random fields: Markova random field (MRF), strict stationary random field (RF) and label field. After defining prior probability distributions which should capture and characterize general and scene-specific knowledge about model parameters and the contextual structure of accurate segmentations, the Bayesian based segmentation framework, which can lead to algorithmic implementation for multiple remotely sensed data, is developed by integrating both the data model and the prior knowledge. To verify the applicability and effectiveness of the proposed segmentation framework, the segmentation algorithms for different types of remotely sensed data are designed within the proposed segmentation framework. The first application relates to SAR intensity image processing, including segmentation and dark spot detection by marked point process. In the second application, the algorithms for LiDAR point cloud segmentation and building detection are developed. Finally, texture and colour texture segmentation problems are tackled within the segmentation framework. All applications demonstrate that the proposed data model provides efficient representations for hierarchical structures hidden in remotely sensed data and the developed segmentation framework leads to successful data processing algorithms for multiple data and task such as segmentation and object detection.

Synthetic aperture radar (SAR) is one of the most versatile tools ever invented for imaging. Due to its better Rayleigh resolution, SAR imaging provides the highest quality radar imagery. These images are used for many applications including but not limited to terrestrial mapping, disaster reconnaissance, medical imaging and military applications. Imaging techniques or geometries which can improve the resolution of the reconstructed imagery is always desired in the SAR imaging. In this dissertation both the linear and nonlinear frequency modulated chirp signals are discussed. The most widely used frequency modulated chirp signal, linear frequency modulated chirp signal, and some of its properties such as spectrum, point spread function and matched filter are summarized. A new nonlinear frequency modulated chirp signal which can be used to improve the image resolution is introduced. In order to validate the offered chirp signal, spotlight SAR imaging geometry together with 2D polar and Stolt format reconstruction algorithms are considered. The synthetic examples are generated using both chirps both with polar and Stolt format processing. Additionally a new change detection method which depends on the idea of generating two different final change maps of the initial and final images in a sequence is offered. The specific algorithms utilized for testing this method are the widely used correlation coefficient change statistic and the intensity ratio change statistic algorithms. This method together with the algorithms mentioned is first applied to synthetic data generated by Stolt format processing. It is shown that the method works on synthetic data. The method together with the algorithms mentioned is also applied to two case studies dfreal disasters, one is 2010 Gulf of Mexico oil spill and the second is 2008 China Sichuan earthquake. It is shown that two final change map method can reduce the false identifications of the changes. Also it is shown that intensity ratio change statistics is a better tool for identifying the changes due to oil contamination. The data used in this study is acquired by Japanese Aerospace Agency's Advanced Land Observing Satellite (ALOS) through Alaska SAR Facility (ASF), at the University of Alaska, Fairbanks.

The capability to observe ocean swell using spaceborne Synthetic Aperture Radar (SAR) has been demonstrated starting with ERS-1 mission in 1992. This dissertation shows how ocean swell properties can be used to combine swell observations of heterogeneous quality and acquired at various times and locations for the observation and forecast of ocean swell fieldsusing ASAR instrument on-board ENVISAT. The first section is a review of how ocean swell spectra can be derived from the SAR complex images of the ocean surface using a quasi-linear transformation. Then, significant swell heights, peak periods and peak directions from in situ measurements are used to assess the accuracy of the SAR observed swell spectra. Using linear propagation in deep ocean, a new swell field reconstruction methodologyis developed in order to gather SAR swell observations related to the same swell field. Propagated from their generation region, these observations render the spatio-temporal properties of the emanating ocean swell fields. Afterwards, a methodology is developed for the exclusion of outliers taking advantage of the swell field consistency. Also, using the irregularly sampled SAR observations, quality controlled estimations of swell field integral parameters are produced on a regular space-time grid. Validation against in situ measurements reveals the dramatic impact of the density of propagated observations on the integral parameters estimated accuracy. Specifically, this parameter is shown to be very dependent on the satellite orbit. Finally, comparisons with the numerical wave model WAVEWATCH-III prove it could potentially benefit from the SAR swell field estimates for assimilation purposes.