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.

Synthetic aperture radar: A focus on current problems

Synthetic aperture radar (SAR) is a powerful tool for mapping and remote sensing. The theory and operation of SAR have seen a period of intense activity in recent years. This paper attempts to review some of the more advanced topics studied in connection with modern SAR systems based on digital processing. Following a brief review of the principles involved in the operation of SAR, attention is focussed on special topics such as advanced SAR modelling and focussing techniques, in particular clutterlock and autofocus, Doppler centroid (DC) estimation methods involving seismic migration technique, moving target imaging, bistatic radar imaging, effects of system nonlinearities, etc.

Near real-time flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, and in urban areas with reasonable accuracy. The accuracy was reduced in urban areas partly because of TerraSAR-X’s restricted visibility of the ground surface due to radar shadow and layover.

Near real-time flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, classifying 89% of flooded pixels correctly, with an associated false positive rate of 6%. Of the urban water pixels visible to TerraSAR-X, 75% were correctly detected, with a false positive rate of 24%. If all urban water pixels were considered, including those in shadow and layover regions, these figures fell to 57% and 18% respectively.

Multipath Reflectivity Estimation in Urban Environments for Synthetic Aperture Radar Images

Synthetic Aperture Radar (SAR) images a target region reflectivity function in the multi-dimensional spatial domain of range and cross-range. SAR synthesizes a large aperture radar in order to achieve a finer azimuth resolution than the one provided by any on-board real antenna. Conventional SAR techniques assume a single reflection of transmitted waveforms from targets. Nevertheless, today¿s new scenes force SAR systems to work in urban environments. Consequently, multiple-bounce returns are added to directscatter echoes. We refer to these as ghost images, since they obscure true target image and lead to poor resolution. By analyzing the quadratic phase error (QPE), this paper demonstrates that Earth¿s curvature influences the defocusing degree of multipath returns. In addition to the QPE, other parameters such as integrated sidelobe ratio (ISLR), peak sidelobe ratio (PSLR), contrast (C) and entropy (E) provide us with the tools to identify direct-scatter echoes in images containing undesired returns coming from multipath.

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.

Multi-temporal monitoring of wetland water levels in the Florida Everglades using interferometric synthetic aperture radar (InSAR)

Interferometric synthetic aperture radar (InSAR) techniques can successfully detect phase variations related to the water level changes in wetlands and produce spatially detailed high-resolution maps of water level changes. Despite the vast details, the usefulness of the wetland InSAR observations is rather limited, because hydrologists and water resources managers need information on absolute water level values and not on relative water level changes. We present an InSAR technique called Small Temporal Baseline Subset (STBAS) for monitoring absolute water level time series using radar interferograms acquired successively over wetlands. The method uses stage (water level) observation for calibrating the relative InSAR observations and tying them to the stage's vertical datum. We tested the STBAS technique with two-year long Radarsat-1 data acquired during 2006–2008 over the Water Conservation Area 1 (WCA1) in the Everglades wetlands, south Florida (USA). The InSAR-derived water level data were calibrated using 13 stage stations located in the study area to generate 28 successive high spatial resolution maps (50 m pixel resolution) of absolute water levels. We evaluate the quality of the STBAS technique using a root mean square error (RMSE) criterion of the difference between InSAR observations and stage measurements. The average RMSE is 6.6 cm, which provides an uncertainty estimation of the STBAS technique to monitor absolute water levels. About half of the uncertainties are attributed to the accuracy of the InSAR technique to detect relative water levels. The other half reflects uncertainties derived from tying the relative levels to the stage stations' datum.

Integration of speckle de-noising and image segmentation using synthetic aperture radar image for flood extent extraction

Flood is one of the detrimental hydro-meteorological threats to mankind. This compels very efficient flood assessment models. In this paper, we propose remote sensing based flood assessment using Synthetic Aperture Radar (SAR) image because of its imperviousness to unfavourable weather conditions. However, they suffer from the speckle noise. Hence, the processing of SAR image is applied in two stages: speckle removal filters and image segmentation methods for flood mapping. The speckle noise has been reduced with the help of Lee, Frost and Gamma MAP filters. A performance comparison of these speckle removal filters is presented. From the results obtained, we deduce that the Gamma MAP is reliable. The selected Gamma MAP filtered image is segmented using Gray Level Co-occurrence Matrix (GLCM) and Mean Shift Segmentation (MSS). The GLCM is a texture analysis method that separates the image pixels into water and non-water groups based on their spectral feature whereas MSS is a gradient ascent method, here segmentation is carried out using spectral and spatial information. As test case, Kosi river flood is considered in our study. From the segmentation result of both these methods are comprehensively analysed and concluded that the MSS is efficient for flood mapping.

Remote sensing of ocean waves by along-track interferometric synthetic aperture radar

A new wave retrieval method for the Along-Track Interferometric Synthetic Aperture Radar (AT-InSAR) phase image is presented. The new algorithm, named parametric retrieval algorithm (PRA), uses the full nonlinear mapping relations. It differs from previous retrieval algorithms in that it does not require a priori information about the sea state or the wind vector from scatterometer data. Instead, it combines the observed AT-InSAR phase spectrum and assumed wind vector to estimate the wind sea spectrum. The method has been validated using several C-band and X-band HH-polarized AT-InSAR observations collocated with spectral buoy measurements. In this paper, X-band and C-band HH-polarized AT-InSAR phase images of ocean waves are first used to study AT-InSAR wave imaging fidelity. The resulting phase spectra are quantitatively compared with forward-mapped in situ directional wave spectra collocated with the AT-InSAR observations. Subsequently, we combine the parametric retrieval algorithm (PRA) with X-band and C-band HH-polarized AT-InSAR phase images to retrieve ocean wave spectra. The results show that the ocean wavelengths, wave directions, and significant wave heights estimated from the retrieved ocean wave spectra are in agreement with the buoy measurements.

On the nonlinear integral transform of an ocean wave spectrum into an along-track interferometric synthetic aperture radar image spectrum

We present a new nonlinear integral transform relating the ocean wave spectrum to the along-track interferometric synthetic aperture radar (AT-INSAR) image spectrum. The AT-INSAR, which is a synthetic aperture radar (SAR) employing two antennas displaced along the platform's flight direction, is considered to be a better instrument for imaging ocean waves than the SAR. This is because the AT-INSAR yields the phase spectrum and not only the amplitude spectrum as with the conventional SAR. While the SAR and AT-INSAR amplitude spectra depend strongly on the modulation of the normalized radar cross section (NRCS) by the long ocean waves, which is poorly known, the phase spectrum depends only weakly on this modulation. By measuring the phase difference between the signals received by both antennas, AT-INSAR measures the radial component of the orbital velocity associated with the ocean waves, which is related to the ocean wave height field by a well-known transfer function. The nonlinear integral transform derived in this paper differs from the one previously derived by Bao et al. [1999] by an additional term containing the derivative of the radial component of the orbital velocity associated with the long ocean waves. By carrying out numerical simulations, we show that, in general, this additional term cannot be neglected. Furthermore, we present two new quasi-linear approximations to the nonlinear integral transform relating the ocean wave spectrum to the AT-INSAR phase spectrum.

A parametric method of retrieving ocean wave spectra from synthetic aperture radar images

A parametric method that extracts the ocean wave directional spectra from synthetic aperture radar (SAR) image is presented. The 180 degrees ambiguity of SAR image and the loss of information beyond the azimuthal cutoff can be overcome with this method. The ocean wave spectra can be obtained from SAR image directly by using iteration inversion mapping method with forward nonlinear mapping. Some numerical experiments have been made by using ERS-1 satellite SAR imagette data. The ocean wave direction retrieved from SAR imagette data is in agreement with the wind direction from the scatterometer data.

Numerical simulation and validation of ocean waves measured by an Along-Track Interferometric Synthetic Aperture Radar

A new nonlinear integral transform of ocean wave spectra into Along-Track Interferometric Synthetic Aperture Radar (ATI-SAR) image spectra is described. ATI-SAR phase image spectra are calculated for various sea states and radar configurations based on the nonlinear integral transform. The numerical simulations show that the slant range to velocity ratio (R/V), significant wave height to ocean wavelength ratio (H-s/lambda), the baseline (2B) and incident angle (theta) affect ATI-SAR imaging. The ATI-SAR imaging theory is validated by means of Two X-band, HH-polarized ATI-SAR phase images of ocean waves and eight C-band, HH-polarized ATI-SAR phase image spectra of ocean waves. It is shown that ATI-SAR phase image spectra are in agreement with those calculated by forward mapping in situ directional wave spectra collected simultaneously with available ATI-SAR observations. ATI-SAR spectral correlation coefficients between observed and simulated are greater than 0.6 and are not sensitive to the degree of nonlinearity. However, the ATI-SAR phase image spectral turns towards the range direction, even if the real ocean wave direction is 30 degrees. It is also shown that the ATI-SAR imaging mechanism is significantly affected by the degree of velocity bunching nonlinearity, especially for high values of R/V and H-s/lambda.

Processing of Synthetic Aperture Radar Images by a Multiscale Boundary Contour System and Feature Contour System

An improved Boundary Contour System (BCS) and Feature Contour System (FCS) neural network model of preattentive vision is applied to large images containing range data gathered by a synthetic aperture radar (SAR) sensor. The goal of processing is to make structures such as motor vehicles, roads, or buildings more salient and more interpretable to human observers than they are in the original imagery. Early processing by shunting center-surround networks compresses signal dynamic range and performs local contrast enhancement. Subsequent processing by filters sensitive to oriented contrast, including short-range competition and long-range cooperation, segments the image into regions. The segmentation is performed by three "copies" of the BCS and FCS, of small, medium, and large scales, wherein the "short-range" and "long-range" interactions within each scale occur over smaller or larger distances, corresponding to the size of the early filters of each scale. A diffusive filling-in operation within the segmented regions at each scale produces coherent surface representations. The combination of BCS and FCS helps to locate and enhance structure over regions of many pixels, without the resulting blur characteristic of approaches based on low spatial frequency filtering alone.

Processing of Synthetic Aperture Radar Images by the Boundary Contour System and Feature Contour System

An improved Boundary Contour System (BCS) and Feature Contour System (FCS) neural network model of preattentive vision is applied to two large images containing range data gathered by a synthetic aperture radar (SAR) sensor. The goal of processing is to make structures such as motor vehicles, roads, or buildings more salient and more interpretable to human observers than they are in the original imagery. Early processing by shunting center-surround networks compresses signal dynamic range and performs local contrast enhancement. Subsequent processing by filters sensitive to oriented contrast, including short-range competition and long-range cooperation, segments the image into regions. Finally, a diffusive filling-in operation within the segmented regions produces coherent visible structures. The combination of BCS and FCS helps to locate and enhance structure over regions of many pixels, without the resulting blur characteristic of approaches based on low spatial frequency filtering alone.