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 basic principle in SAS is to use an array which is small in length to create a long synthetic array thus the better resolution is achieved through the use of signal processing. Additionally, the resolution that is independent of range and signal frequency, makes SAS a advantageous tool for applications. Although the origin of SAS comes from SAR, SAS still needs to overcome all constraints for real-world application. In a previous study by Sung and prof. Liu, published results of the along track resolution experiments which were well done however there was still much room in range resolution, the purpose of this research is to achieve high range resolution at any ranges. Indeed there are many existing factors affecting the capability of resolution which including characteristic of the target, certain arrangements of targets, bandwidth, waveforms and pulse duration and etc. High range resolution is obtained using pulse compression techniques. The experiments were carried out using the transducers of AST MK VI 192 kHz which were employed to transmit and receive signals, scanned various copper balls at anechoic water tank(4 m ¡Ñ 3.5 m ¡Ñ 2 m) in NSYSU. From the equipment we have now results were evaluated based on both simulated and real data: for the range resolution the pulse length is very important the shortest pulse length on an object would be 2L/c theoretically, the measured range resolution is about 7.5 cm for the 20-kHz bandwidth signals and 5 cm for along track resolution. As all the experiments have been successful in the Water Tank, we intent to launch further investigation of this research to the real world application of SAS i.e. in Sizihwan Bay Marine Test Field.

In this thesis a stepped-frequency Inverse Synthetic Aperture Radar (ISAR) is employed to develop two-dimensional range-Doppler images of a small ghter aircraft which exhibits three dimensional rotational rotation. The simulation is designed such that the target can exhibit yaw, pitch and roll motions at the same time. First, radar returns from prominent scatterers of various parts of the target are processed and displayed using conventional Fourier transform. The eects of dierent complex motion types and scenarios are observed and discussed. Then, several linear and bi-linear time-frequency distributions including shorttime Fourier transform, Wigner-Ville, pseudo Wigner-Ville, smoothed pseudo Wigner-Ville, Choi-Williams, Born-Jordan and Zhao-Atlas-Marks distributions are applied to the same target and scenarios. The performance of the transforms is compared for each scenario. The reasons for success of the distributions are discussed thoroughly.

A Bussgang based blind deconvolution algorithm called self-correcting multi-channel Bussgang (SCMB) blind deconvolution algorithm was proposed. Unlike the original Bussgang blind deconvolution algorithm where the probability density function (pdf) of the signal being recovered is assumed to be completely known, the proposed SCMB blind deconvolution algorithm relaxes this restriction by parameterized the pdf with a Gaussian mixture model and expectation maximization (EM) algorithm, an iterative maximum likelihood approach, is employed to estimate the parameter side by side with the estimation of the equalization filters of the original Bussgang blind deconvolution algorithm. A feedback loop is also designed to compensate the effect of the parameter estimation error on the estimation of the equalization filters. Application of the SCMB blind deconvolution framework for binary image restoration, multi-pass synthetic aperture radar (SAR) autofocus and inverse synthetic aperture radar (ISAR) autofocus are exploited with great results.

Thesis (Ph. D.)--University of Florida, 2001. / Title from first page of PDF file. Document formatted into pages; contains xi, 124 p.; also contains graphics. Vita. Includes bibliographical references (p. 120-123).

Synthetic Aperture Radar (SAR) has been identified as a good candidate to provide high-resolution soil moisture information over extended areas. SAR data could be used as observations within a global Data Assimilation (DA) approach to benefit applications such as hydrology and agriculture. Prior to developing an operational DA system, one must tackle the following challenges of soil moisture estimation with SAR: (1) the dependency of the measured radar signal on both soil moisture and soil surface roughness which leads to an ill-conditioned inverse problem, and (2) the difficulty in characterizing spatially/temporally surface roughness of natural soils and its scattering contribution. The objectives of this project are (1) to develop a roughness measurement method to improve the spatial/temporal characterization of soil surface roughness, and (2) to investigate to what extent the inverse problem can be solved by combining multipolarization, multi-incidence, and/or multi-frequency radar measurements. The first objective is achieved with a measurement method based on Structure from Motion (SfM). It is tailored to monitor natural surface roughness changes which have often been assumed negligible although without evidence. The measurement method is flexible, a.ordable, straightforward and generates Digital Elevation Models (DEMs) for a SAR-pixel-size plot with mm accuracy. A new processing method based on band-filtering of the DEM and its 2D Power Spectral Density (PSD) is proposed to compute the classical roughness parameters. Time series of DEMs show that non-negligible changes in surface roughness can happen within two months at scales relevant for microwave scattering. The second objective is achieved using maximum likelihood fitting of the Oh backscattering model to (1) full-polarimetric Radarsat-2 data and (2) simulated multi-polarization / multi-incidence / multi-frequency radar data. Model fitting with the Radarsat-2 images leads to poor soil moisture retrieval which is related to inaccuracy of the Oh model. Model fitting with the simulated data quantifies the amount of multilooking for di.erent combinations of measurements needed to mitigate the critical e.ect of speckle on soil moisture uncertainty. Results also suggest that dual-polarization measurements at L- and C-bands are a promising combination to achieve the observation requirements of soil moisture. In conclusion, the SfM method along with the recommended processing techniques are good candidates to improve the characterization of surface roughness. A combination of multi-polarization and multi-frequency radar measurements appears to be a robust basis for a future Data Assimilation system for global soil moisture monitoring.

This research seeks to expand the role of variational and adjoint processing methods into segments of the sonar, radar, and nondestructive testing communities where they have not yet been widely introduced. First, synthetic aperture reconstruction is expressed in terms of the adjoint operator. Many, if not all, practical imaging modalities can be traced back to this general result, as the adjoint is the foundation for backprojection-type algorithms. Next, active surfaces are developed in the context of the Helmholtz equation for the cases of opaque scatterers (i.e., with no interior field) embedded in free space, and penetrable scatterers embedded in a volume which may be bounded. The latter are demonstrated numerically using closed-form solutions based on spherical harmonics. The former case was chosen as the basis for a laboratory experiment using Lamb waves in an aluminum plate. Lamb wave propagation in plates is accurately described by the Helmholtz equation, where the field quantity is the displacement potential. However, the boundary conditions associated with the displacement potential formulation of Lamb waves are incompatible with the shape gradient derived for the Helmholtz equation, except for very long or very short wavelengths. Lastly, optical flow is used to solve a new and unique problem in the field of synthetic aperture sonar. Areas of acoustic focusing and dilution attributable to refraction can sometimes resemble the natural bathymetry of the ocean floor. The difference is often visually indistinguishable, so it is desirable to have a means of detecting these transient refractive effects without having to repeat the survey. Optical flow proved to be effective for this purpose, and it is shown that the parameters used to control the algorithm can be linked to known properties of the data collection and scattering physics.

Synthetic aperture radar (SAR) is an imaging technique based on the radio reflectivity of the target being imaged. SAR instruments offer many advantages over optical imaging due to the ability to form coherent images in inclement weather, at night, and through ground cover. High resolution is achieved in azimuth through a synthesized aperture much larger than the physical antenna of the imaging device. Consequently, proper focusing requires accurate information about the relative motion between the antenna phase center and the scene. Any unknown target velocity, acceleration, rotation, or vibration will introduce errors in the image. This work addresses a novel method of focusing a moving target in a SAR image through the estimation of various motion parameters. The target azimuth position is determined through monopulse radar, at which point range velocity and acceleration are estimated across a series of overlapping sub-apertures. Cross-range velocity is then estimated through a search to optimize an image quality metric such as entropy or contrast. A final focused image is then generated based on this velocity vector. Methods of extending this work for a single phase center system are considered. This technique is demonstrated with real radar data from an experimental system, and the performance of this technique is compared both subjectively and with a variety of image metrics to the MITRE keystone technique. Finally, extensions to this current line of research are considered. / text

This thesis investigates the sensitivity and separability of post-harvest tillage conditions using polarimetric Synthetic Aperture RADAR in southwestern Ontario. Variables examined include: linear polarizations HH, HV, and VV and polarimetric variables: pedestal height, co-polarized complex correlation coefficient magnitude, left and right co-polarized circular polarizations and co-polarized phase difference. Six fine-quad polarimetric, high incidence angle (49°) RADARSAT-2 images acquired over three dates in fall 2010 were used. Over 100 fields were monitored, coincident with satellite overpasses. OMAFRA’s AgRI, a high-resolution polygon network was used to extract average response from fields. Discrimination between tillage practices was best later in the fall season, due to sample size and low soil moisture conditions. Variables most sensitive to tillage activities include HH and VV polarizations and co-polarized complex correlation coefficient magnitude. A supervised support vector machine (SVM) classifier classified no-till and conventional tillage with 91.5% overall accuracy. These results highlight the potential of RADARSAT-2 for monitoring tillage conditions.