SAR Wave INteraction for Natural Targets Over Land (SWINTOL) is a project funded by the European Space Agency. The study’s goal is to better understand the interaction of high frequency radar (> X-band) with vegetation and soils, in order to drive the development of a high-frequency electromagnetic model to simulate SAR imagery at high resolution (< 1 m). Existing models work well at C and X band frequencies, but do not work properly at higher frequencies. Cranfield University’s role in this project was to provide the field data necessary for model validation and development. Radar imagery was taken of a barley crop over an entire growing season. The portable outdoor GB-SAR system used the tomographic profiling (TP) technique to capture polarimetric imagery of the crop. TP is a scheme that provides detailed maps of the vertical backscatter pattern through a crop canopy, along a narrow transect directly beneath the radar platform. Fully-polarimetric imagery was obtained across overlapping 6.5 GHz bandwidths over the X- and Ku-band frequency range 8-20 GHz. This gave the opportunity to see the detailed scattering behaviour within the crop at the plant component level, from emergence of the crop through to harvesting. In combination with the imagery, full bio-geophysical characterisation of the crop and soil was made on each measurement date. Surface roughness characterisation of the soil was captured using a 3D optical stereoscopic system. This work details the measurements made, and provides a comparative assessment of the results in terms of understanding the backscatter in relation to biophysical and radar parameters.

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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

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.

Since the 1980s, satellite-borne synthetic aperture radar (SAR) has been investigated for early warning and monitoring of marine oil spills to permit effective satellite surveillance in the marine environment. Automated detection of oil spills from satellite SAR intensity imagery consists of three steps: 1) Detection of dark spots; 2) Extraction of features from the detected dark spots; and 3) Classification of the dark spots into oil spills and look-alikes. However, marine oil spill detection is a very difficult and challenging task. Open questions exist in each of the three stages. In this thesis, the focus is on the first stage—dark spot detection. An efficient and effective dark spot detection method is critical and fundamental for developing an automated oil spill detection system. A novel method for this task is presented. The key to the method is utilizing the spatial density feature to enhance the separability of dark spots and the background. After an adaptive intensity thresholding, a spatial density thresholding is further used to differentiate dark spots from the background. The proposed method was applied to a evaluation dataset with 60 RADARSAT-1 ScanSAR Narrow Beam intensity images containing oil spill anomalies. The experimental results obtained from the test dataset demonstrate that the proposed method for dark spot detection is fast, robust and effective. Recommendations are given for future research to be conducted to ensure that this procedure goes beyond the prototype stage and becomes a practical application.

Ultra-wideband (UWB) technology and its use in imaging and sensing have drawnsignicant interest in the last two decades. Extensive studies have contributed toutilize UWB transient scattering for automated target recognition and imagingpurposes. In this thesis a near-eld UWB synthetic aperture radar (SAR) imagingalgorithm is presented.It is shown with measurements and simulation, that it is possible to reconstruct an imageof an object in the near eld region using UWB technology and SAR imaging algorithm.However the nal SAR image is highly aected by unwanted scattered elds at each pixelusually observed as an image artifact in the nal image. In this study these artifactsare seen as a smile around the main object. Two methods are suggested in this thesiswork to suppress this artifact. The rst method combines the scattered eld informationreceived from both rear and front of the object to reconstruct two separate images, onefrom rear view and one from front view of the object respectively. Since the scatteredelds from behind the object are mirrored, the pixel by pixel multiplication of thesetwo images for objects with simple geometry will cancel the artifact. This method isvery simple and fast applicable to objects with simple geometry. However this methodcannot be used for objects with rather complex geometry and boundaries. Thereforethe Range Point Migration (RPM) method is used along with the global characteristicsof the observed range map to introduce a new artifact rejection method based on thedirectional of arrival (DOA) of scattered elds at each pixel. DOA information can beused to calculate an optimum theta for each antenna. This optimum angle along withthe real physical direction of arrival at each position can produce a weighting factor thatlater can be used to suppress the eect of undesired scattered elds producing the smileshaped artifact. Final results of this study clearly show that the UWB SAR accompaniedwith DOA can produce an image of an object free of undesired artifact from scatteredeld of adjacent antennas.

Since the 1980s, satellite-borne synthetic aperture radar (SAR) has been investigated for early warning and monitoring of marine oil spills to permit effective satellite surveillance in the marine environment. Automated detection of oil spills from satellite SAR intensity imagery consists of three steps: 1) Detection of dark spots; 2) Extraction of features from the detected dark spots; and 3) Classification of the dark spots into oil spills and look-alikes. However, marine oil spill detection is a very difficult and challenging task. Open questions exist in each of the three stages. In this thesis, the focus is on the first stage—dark spot detection. An efficient and effective dark spot detection method is critical and fundamental for developing an automated oil spill detection system. A novel method for this task is presented. The key to the method is utilizing the spatial density feature to enhance the separability of dark spots and the background. After an adaptive intensity thresholding, a spatial density thresholding is further used to differentiate dark spots from the background. The proposed method was applied to a evaluation dataset with 60 RADARSAT-1 ScanSAR Narrow Beam intensity images containing oil spill anomalies. The experimental results obtained from the test dataset demonstrate that the proposed method for dark spot detection is fast, robust and effective. Recommendations are given for future research to be conducted to ensure that this procedure goes beyond the prototype stage and becomes a practical application.

The synthetic aperture radar (SAR) onboard Earth observing satellites has been acknowledged as an integral tool for many applications in monitoring the marine environment. Some of these applications include regional sea-ice monitoring and detection of illegal or accidental oil discharges from ships. Nonetheless, a practicality of the usage of SAR images is greatly hindered by the presence of speckle noises. Such noise must be eliminated or reduced to be utilized in real-world applications to ensure the safety of the marine environment. Thus this thesis presents a novel two-phase total variation optimization segmentation approach to tackle such a challenging task. In the total variation optimization phase, the Rudin-Osher-Fatemi total variation model was modified and implemented iteratively to estimate the piecewise smooth state by minimizing the total variation constraints. In the finite mixture model classification phase, an expectation-maximization method was performed to estimate the final class likelihoods using a Gaussian mixture model. Then a maximum likelihood classification technique was utilized to obtain the final segmented result. For its evaluation, a synthetic image was used to test its effectiveness. Then it was further applied to two distinct real SAR images, X-band COSMO-SkyMed imagery containing verified oil-spills and C-band RADARSAT-2 imagery mainly containing two different sea-ice types to confirm its robustness. Furthermore, other well-established methods were compared with the proposed method to ensure its performance. With the advantage of a short processing time, the visual inspection and quantitative analysis including kappa coefficients and F1 scores of segmentation results confirm the superiority of the proposed method over other existing methods.

The synthetic aperture radar (SAR) onboard Earth observing satellites has been acknowledged as an integral tool for many applications in monitoring the marine environment. Some of these applications include regional sea-ice monitoring and detection of illegal or accidental oil discharges from ships. Nonetheless, a practicality of the usage of SAR images is greatly hindered by the presence of speckle noises. Such noise must be eliminated or reduced to be utilized in real-world applications to ensure the safety of the marine environment. Thus this thesis presents a novel two-phase total variation optimization segmentation approach to tackle such a challenging task. In the total variation optimization phase, the Rudin-Osher-Fatemi total variation model was modified and implemented iteratively to estimate the piecewise smooth state by minimizing the total variation constraints. In the finite mixture model classification phase, an expectation-maximization method was performed to estimate the final class likelihoods using a Gaussian mixture model. Then a maximum likelihood classification technique was utilized to obtain the final segmented result. For its evaluation, a synthetic image was used to test its effectiveness. Then it was further applied to two distinct real SAR images, X-band COSMO-SkyMed imagery containing verified oil-spills and C-band RADARSAT-2 imagery mainly containing two different sea-ice types to confirm its robustness. Furthermore, other well-established methods were compared with the proposed method to ensure its performance. With the advantage of a short processing time, the visual inspection and quantitative analysis including kappa coefficients and F1 scores of segmentation results confirm the superiority of the proposed method over other existing methods.

This thesis evaluates the potential for using line of sight returns and return signals from underneath a forest canopy using X-band, airborne synthetic aperture radar (SAR) tomography. Approximately 30% of the Earth’s land surface is covered by vegetation, therefore global digital elevation models (DEMs) contain a signal from the forest canopy and not the ground. By uncovering new techniques to find the ground signals, using data collected from airborne platforms as verification, such procedures could be applied to currently operational and future X-band, spaceborne systems with the aim of resolving much of the vegetation bias on an international scale. Data from three sources is presented; data collected from Selex ES’s SAR systems, the GOTCHA dataset and simulated data. Before carrying out tomography it is shown that SAR interferometry (InSAR) can successfully be applied to X-band, helicopter data. A scatterer defined as a candidate persistent scatterer (CPS) is introduced, where the pixels are stable and coherent over a matter of days. An algorithm for selecting CPSs is developed by exploiting sparsity and a novel choice of hard thresholding operator. Using simulated forestry and SAR information the effects of changing input parameters on the outcome of the tomographic profile is analysed. What is found in this study is that model simulations demonstrate that ground points can be detected if the platform motion is relatively stable and that temporal decorrelation over the forest volume is kept to a minimal. An understory can confuse the tomographic profile since less line of sight observations can be made. By combining line of sight observations alongside new tomography techniques on high resolution SAR data this thesis shows it is possible to detect ground scatterers, even at X-band.