Ocean wave imaging mechanism by imaging radar

Analytical representations of the high frequency spectra of ocean wave and its variation due to the variation of ocean surface current are derived from the wave-number spectrum balance equation. The ocean surface imaging formulation of real aperture radar (RAR) is given using electromagnetic wave backscattering theory of ocean surface and the modulations of ocean surface winds, currents and their variations to RAR are described. A general representation of the phase modulation induced by the ocean surface motion is derived according to standard synthetic aperture radar (SAR) imaging theory. The detectability of ocean current and sea bottom topography by imaging radar is discussed. The results constitute the theoretical basis for detecting ocean wave fields, ocean surface winds, ocean surface current fields, sea bottom topography, internal wave and so on.

Radar cross-section enhancement of dihedral corner reflector using fractal-based metallo-dielectric structures

Effective use of fractal-based metallo-dielectric structures for enhancing the radar cross-section (RCS) of dihedral corner reflectors is reported. RCS enhancement of about 30 dBsm is obtained for corner reflectors with corner angles other than 90deg. This may find application in remote sensing and synthetic aperture radar.

Sensitivity of radar backscatter to desert surface roughness

Synthetic aperture radar (SAR) data have proved useful in remote sensing studies of deserts, enabling different surfaces to be discriminated by differences in roughness properties. Roughness is characterized in SAR backscatter models using the standard deviation of surface heights (sigma), correlation length (L) and autocorrelation function (rho(xi)). Previous research has suggested that these parameters are of limited use for characterizing surface roughness, and are often unreliable due to the collection of too few roughness profiles, or under-sampling in terms of resolution or profile length (L-p). This paper reports on work aimed at establishing the effects of L-p and sampling resolution on SAR backscatter estimations and site discrimination. Results indicate significant relationships between the average roughness parameters and L-p, but large variability in roughness parameters prevents any clear understanding of these relationships. Integral equation model simulations demonstrate limited change with L-p and under-estimate backscatter relative to SAR observations. However, modelled and observed backscatter conform in pattern and magnitude for C-band systems but not for L-band data. Variation in surface roughness alone does not explain variability in site discrimination. Other factors (possibly sub-surface scattering) appear to play a significant role in controlling backscatter characteristics at lower frequencies.

Co-eruptive and inter-eruptive surface deformation measured by satellite radar interferometry at Nyamuragira volcano, D.R. Congo, 1996 to 2010.

Interferometric Synthetic Aperture Radar (InSAR) measurements of surface deformation at Nyamuragira Volcano between 1996 and 2010 reveal a variety of co-eruptive and inter-eruptive signals. During 7 of the 8 eruptions in this period deformation was measured that is consistent with the emplacement of shallow near-vertical dykes feeding the eruptive fissures and associated with a NNW-trending fissure zone that traverses the summit caldera. Between eruptions the caldera and the summit part of this fissure zone subsided gradually (b3–5 cm/year). We also find evidence of post-eruption subsidence around the sites of the main vents of some flank eruptions (2002, 2004, 2006, and 2010). In the 6 months prior to the 2010 eruption a10-km wide zone centred on the caldera inflated by 1–2 cm. The low magnitude of this signal suggests that the presumed magma reservoir at 3–8 km depth contains highly compressible magma with little stored elastic strain energy. To the north of the caldera the fissure zone splits into WNW and NE branches around a zone that has a distinct InSAR signal. We interpret this zone to represent an elevated, 'stable' block of basement rocks buried by lavas within the Rift Zone.

Improving synthetic aperture image by image compounding in beamforming process

In this work, signal processing techniques are used to improve the quality of image based on multi-element synthetic aperture techniques. Using several apodization functions to obtain different side lobes distribution, a polarity function and a threshold criterium are used to develop an image compounding technique. The spatial diversity is increased using an additional array, which generates complementary information about the defects, improving the results of the proposed algorithm and producing high resolution and contrast images. The inspection of isotropic plate-like structures using linear arrays and Lamb waves is presented. Experimental results are shown for a 1-mm-thick isotropic aluminum plate with artificial defects using linear arrays formed by 30 piezoelectric elements, with the low dispersion symmetric mode S0 at the frequency of 330 kHz. © 2011 American Institute of Physics.

Modelado y Mitigación de Fenómenos de Multitrayecto en Imágenes Radar mediante Técnicas basadas en Inversión Temporal

Las técnicas SAR (Synthetic Aperture Radar, radar de apertura sintética) e ISAR (Inverse SAR, SAR inverso) son sistemas radar coherentes de alta resolución, capaces de proporcionar un mapa de la sección radar del blanco en el dominio espacial de distancia y acimut. El objetivo de ambas técnicas radica en conseguir una resolución acimutal más fina generando una apertura sintética a partir del movimiento relativo entre radar y blanco. Los radares imagen complementan la labor de los sistemas ópticos e infrarrojos convencionales, especialmente en condiciones meteorológicas adversas. Los sistemas SAR e ISAR convencionales se diseñan para iluminar blancos en situaciones de línea de vista entre sensor y blanco. Por este motivo, presentan un menor rendimiento en escenarios complejos, como por ejemplo en bosques o entornos urbanos, donde los retornos multitrayecto se superponen a los ecos directos procedentes de los blancos. Se conocen como "imágenes fantasma", puesto que enmascaran a los verdaderos blancos y dan lugar a una calidad visual pobre, complicando en gran medida la detección del blanco. El problema de la mitigación del multitrayecto en imágenes radar adquiere una relevancia teórica y práctica. En esta Tesis Doctoral, se hace uso del concepto de inversión temporal (Time Reversal, TR) para mejorar la calidad visual de las imágenes SAR e ISAR eliminando las "imágenes fantasma" originadas por la propagación multitrayecto (algoritmos TR-SAR y TR-ISAR, respectivamente). No obstante, previamente a la aplicación de estas innovadoras técnicas de mitigación del multi-trayecto, es necesario resolver el problema geométrico asociado al multitrayecto. Centrando la atención en la mejora de las prestaciones de TR-ISAR, se implementan una serie de técnicas de procesado de señal avanzadas antes y después de la etapa basada en inversión temporal (el eje central de esta Tesis). Las primeras (técnicas de pre-procesado) están relacionadas con el multilook averaging, las transformadas tiempo-frecuencia y la transformada de Radon, mientras que las segundas (técnicas de post-procesado) se componen de un conjunto de algoritmos de superresolución. En pocas palabras, todas ellas pueden verse como un valor añadido al concepto de TR, en lugar de ser consideradas como técnicas independientes. En resumen, la utilización del algoritmo diseñado basado en inversión temporal, junto con algunas de las técnicas de procesado de señal propuestas, no deben obviarse si se desean obtener imágenes ISAR de gran calidad en escenarios con mucho multitrayecto. De hecho, las imágenes resultantes pueden ser útiles para posteriores esquemas de reconocimiento automático de blancos (Automatic Target Recognition, ATR). Como prueba de concepto, se hace uso tanto de datos simulados como experimentales obtenidos a partir de radares de alta resolución con el fin de verificar los métodos propuestos.

Spatiotemporal characteristics of the Huangtupo landslide in the Three Gorges region (China) constrained by radar interferometry

The Huangtupo landslide is one of the largest in the Three Gorges region, China. The county-seat town of Badong, located on the south shore between the Xiling and Wu gorges of the Yangtze River, was moved to this unstable slope prior to the construction of the Three Gorges Project, since the new Three Gorges reservoir completely submerged the location of the old city. The instability of the slope is affecting the new town by causing residential safety problems. The Huangtupo landslide provides scientists an opportunity to understand landslide response to fluctuating river water level and heavy rainfall episodes, which is essential to decide upon appropriate remediation measures. Interferometric Synthetic Aperture Radar (InSAR) techniques provide a very useful tool for the study of superficial and spatially variable displacement phenomena. In this paper, three sets of radar data have been processed to investigate the Huangtupo landslide. Results show that maximum displacements are affecting the northwest zone of the slope corresponding to Riverside slumping mass I#. The other main landslide bodies (i.e. Riverside slumping mass II#, Substation landslide and Garden Spot landslide) exhibit a stable behaviour in agreement with in situ data, although some active areas have been recognized in the foot of the Substation landslide and Garden Spot landslide. InSAR has allowed us to study the kinematic behaviour of the landslide and to identify its active boundaries. Furthermore, the analysis of the InSAR displacement time-series has helped recognize the different displacement patterns on the slope and their relationships with various triggering factors. For those persistent scatterers, which exhibit long-term displacements, they can be decomposed into a creep model (controlled by geological conditions) and a superimposed recoverable term (dependent on external factors), which appears closely correlated with reservoir water level changes close to the river's edge. These results, combined with in situ data, provide a comprehensive analysis of the Huangtupo landslide, which is essential for its management.

A novel method for studying the cortical processing of human swallowing using Synthetic Aperture Magnetometry(SAM)

Background/Aims: Positron emission tomography has been applied to study cortical activation during human swallowing, but employs radio-isotopes precluding repeated experiments and has to be performed supine, making the task of swallowing difficult. Here we now describe Synthetic Aperture Magnetometry (SAM) as a novel method of localising and imaging the brain's neuronal activity from magnetoencephalographic (MEG) signals to study the cortical processing of human volitional swallowing in the more physiological prone position. Methods: In 3 healthy male volunteers (age 28–36), 151-channel whole cortex MEG (Omega-151, CTF Systems Inc.) was recorded whilst seated during the conditions of repeated volitional wet swallowing (5mls boluses at 0.2Hz) or rest. SAM analysis was then performed using varying spatial filters (5–60Hz) before co-registration with individual MRI brain images. Activation areas were then identified using standard sterotactic space neuro-anatomical maps. In one subject repeat studies were performed to confirm the initial study findings. Results: In all subjects, cortical activation maps for swallowing could be generated using SAM, the strongest activations being seen with 10–20Hz filter settings. The main cortical activations associated with swallowing were in: sensorimotor cortex (BA 3,4), insular cortex and lateral premotor cortex (BA 6,8). Of relevance, each cortical region displayed consistent inter-hemispheric asymmetry, to one or other hemisphere, this being different for each region and for each subject. Intra-subject comparisons of activation localisation and asymmetry showed impressive reproducibility. Conclusion: SAM analysis using MEG is an accurate, repeatable, and reproducible method for studying the brain processing of human swallowing in a more physiological manner and provides novel opportunities for future studies of the brain-gut axis in health and disease.

The investigation of oscillatory changes in the visual cortex using synthetic aperture magnetometry

This thesis is an exploration of the oscillatory changes occurring in the visual cortex as measured by a functional imaging technique known as Synthetic Aperture Magnetometry (SAM), and how these compare to the BOLD response, across a number of different experimental paradigms. In chapter one the anatomy and physiology of the visual pathways and cortex are outlined, introducing the reader to structures and terms used throughout the thesis whilst chapter two introduces both the technology and analysis techniques required to record MEG and fMRI and also outlines the theory behind SAM. In chapter three the temporal frequency tuning of both striate and extrastriate cortex is investigated, showing fundamental differences in both tuning characteristics and oscillatory power changes between the two areas. Chapter four introduces the concept of implied-motion and investigates the role of area V5 / MT in the perception of such stimuli and shows, for the first time, the temporal evolution of the response in this area. Similarly a close link is shown between the early evoked potential, produced by the stimulus, and previous BOLD responses. Chapter five investigates the modulation of cortical oscillations to both shifts in attention and varying stimulus contrast. It shows that there are both induced and evoked modulation changes with attention, consistent with areas previously known to show BOLD responses. Chapter six involves a direct comparison of cortical oscillatory changes with those of the BOLD response in relation to the parametric variation of a motion coherence stimulus. It is shown that various cortical areas show a linear BOLD response to motion coherence and, for the first time, that both induced oscillatory and evoked activity also vary linearly in areas coincidental with the BOLD response. The final chapter is a summary of the main conclusions and suggests further work.

Analysis and Optimization of Synthetic Aperture Ultrasound Imaging Using the Effective Aperture Approach

An effective aperture approach is used as a tool for analysis and parameter optimization of mostly known ultrasound imaging systems - phased array systems, compounding systems and synthetic aperture imaging systems. Both characteristics of an imaging system, the effective aperture function and the corresponding two-way radiation pattern, provide information about two of the most important parameters of images produced by an ultrasound system - lateral resolution and contrast. Therefore, in the design, optimization of the effective aperture function leads to optimal choice of such parameters of an imaging systems that influence on lateral resolution and contrast of images produced by this imaging system. It is shown that the effective aperture approach can be used for optimization of a sparse synthetic transmit aperture (STA) imaging system. A new two-stage algorithm is proposed for optimization of both the positions of the transmitted elements and the weights of the receive elements. The proposed system employs a 64-element array with only four active elements used during transmit. The numerical results show that Hamming apodization gives the best compromise between the contrast of images and the lateral resolution.

Retrieval of grassland biophysical parameters using multitemporal optical and radar satellite data

The amount and quality of available biomass is a key factor for the sustainable livestock industry and agricultural management related decision making. Globally 31.5% of land cover is grassland while 80% of Ireland’s agricultural land is grassland. In Ireland, grasslands are intensively managed and provide the cheapest feed source for animals. This dissertation presents a detailed state of the art review of satellite remote sensing of grasslands, and the potential application of optical (Moderate–resolution Imaging Spectroradiometer (MODIS)) and radar (TerraSAR-X) time series imagery to estimate the grassland biomass at two study sites (Moorepark and Grange) in the Republic of Ireland using both statistical and state of the art machine learning algorithms. High quality weather data available from the on-site weather station was also used to calculate the Growing Degree Days (GDD) for Grange to determine the impact of ancillary data on biomass estimation. In situ and satellite data covering 12 years for the Moorepark and 6 years for the Grange study sites were used to predict grassland biomass using multiple linear regression, Neuro Fuzzy Inference Systems (ANFIS) models. The results demonstrate that a dense (8-day composite) MODIS image time series, along with high quality in situ data, can be used to retrieve grassland biomass with high performance (R2 = 0:86; p < 0:05, RMSE = 11.07 for Moorepark). The model for Grange was modified to evaluate the synergistic use of vegetation indices derived from remote sensing time series and accumulated GDD information. As GDD is strongly linked to the plant development, or phonological stage, an improvement in biomass estimation would be expected. It was observed that using the ANFIS model the biomass estimation accuracy increased from R2 = 0:76 (p < 0:05) to R2 = 0:81 (p < 0:05) and the root mean square error was reduced by 2.72%. The work on the application of optical remote sensing was further developed using a TerraSAR-X Staring Spotlight mode time series over the Moorepark study site to explore the extent to which very high resolution Synthetic Aperture Radar (SAR) data of interferometrically coherent paddocks can be exploited to retrieve grassland biophysical parameters. After filtering out the non-coherent plots it is demonstrated that interferometric coherence can be used to retrieve grassland biophysical parameters (i. e., height, biomass), and that it is possible to detect changes due to the grass growth, and grazing and mowing events, when the temporal baseline is short (11 days). However, it not possible to automatically uniquely identify the cause of these changes based only on the SAR backscatter and coherence, due to the ambiguity caused by tall grass laid down due to the wind. Overall, the work presented in this dissertation has demonstrated the potential of dense remote sensing and weather data time series to predict grassland biomass using machine-learning algorithms, where high quality ground data were used for training. At present a major limitation for national scale biomass retrieval is the lack of spatial and temporal ground samples, which can be partially resolved by minor modifications in the existing PastureBaseIreland database by adding the location and extent ofeach grassland paddock in the database. As far as remote sensing data requirements are concerned, MODIS is useful for large scale evaluation but due to its coarse resolution it is not possible to detect the variations within the fields and between the fields at the farm scale. However, this issue will be resolved in terms of spatial resolution by the Sentinel-2 mission, and when both satellites (Sentinel-2A and Sentinel-2B) are operational the revisit time will reduce to 5 days, which together with Landsat-8, should enable sufficient cloud-free data for operational biomass estimation at a national scale. The Synthetic Aperture Radar Interferometry (InSAR) approach is feasible if there are enough coherent interferometric pairs available, however this is difficult to achieve due to the temporal decorrelation of the signal. For repeat-pass InSAR over a vegetated area even an 11 days temporal baseline is too large. In order to achieve better coherence a very high resolution is required at the cost of spatial coverage, which limits its scope for use in an operational context at a national scale. Future InSAR missions with pair acquisition in Tandem mode will minimize the temporal decorrelation over vegetation areas for more focused studies. The proposed approach complements the current paradigm of Big Data in Earth Observation, and illustrates the feasibility of integrating data from multiple sources. In future, this framework can be used to build an operational decision support system for retrieval of grassland biophysical parameters based on data from long term planned optical missions (e. g., Landsat, Sentinel) that will ensure the continuity of data acquisition. Similarly, Spanish X-band PAZ and TerraSAR-X2 missions will ensure the continuity of TerraSAR-X and COSMO-SkyMed.

A Generalized Approach to Multiresolution Complex SAR Signal Processing

Multiresolution synthetic aperture radar (SAR) image formation has been proven to be beneficial in a variety of applications such as improved imaging and target detection as well as speckle reduction. SAR signal processing traditionally carried out in the Fourier domain has inherent limitations in the context of image formation at hierarchical scales. We present a generalized approach to the formation of multiresolution SAR images using biorthogonal shift-invariant discrete wavelet transform (SIDWT) in both range and azimuth directions. Particularly in azimuth, the inherent subband decomposition property of wavelet packet transform is introduced to produce multiscale complex matched filtering without involving any approximations. This generalized approach also includes the formulation of multilook processing within the discrete wavelet transform (DWT) paradigm. The efficiency of the algorithm in parallel form of execution to generate hierarchical scale SAR images is shown. Analytical results and sample imagery of diffuse backscatter are presented to validate the method.