Self-correcting multi-channel Bussgang blind deconvolution using expectation maximization (EM) algorithm and feedback

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.Ph.D.Committee Chair: Dr. Russell Mersereau; Committee Member: Dr. Doug Willams; Committee Member: Dr. Mark Richards; Committee Member: Dr. Xiaoming Huo; Committee Member: Dr. Ye (Geoffrey) L

Image Restoration for Remote Sensing: Overview and Toolbox

Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (both active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering the true unknown image from the degraded observed image. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques in different paths according to each sensor type. This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration in the remote sensing community. We, therefore, provide a comprehensive, discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to investigate the vibrant topic of data restoration by supplying sufficient detail and references. Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community. The toolboxes are provided in https://github.com/ImageRestorationToolbox.Comment: This paper is under review in GRS

Phase Relaxed Localized Excitation Pulses for Inner Volume Fast Spin Echo Imaging

PURPOSE: To design multidimensional spatially selective radiofrequency (RF) pulses for inner volume imaging (IVI) with three‐dimensional (3D) fast spin echo (FSE) sequences. Enhanced background suppression is achieved by exploiting particular signal properties of FSE sequences. THEORY AND METHODS: The CPMG condition dictates that echo amplitudes will rapidly decrease if a 90° phase difference between excitation and refocusing pulses is not present, and refocusing flip angles are not precisely 180°. This mechanism is proposed as a means for generating additional background suppression for spatially selective excitation, by biasing residual excitation errors toward violating the CPMG condition. 3D RF pulses were designed using this method with a 3D spherical spiral trajectory, under‐sampled by factor 5.6 for an eight‐channel PTx system, at 3 Tesla. RESULTS: 3D‐FSE IVI with pulse durations of approximately 12 ms was demonstrated in phantoms and for T(2)‐weighted brain imaging in vivo. Good image quality was obtained, with mean background suppression factors of 103 and 82 ± 6 in phantoms and in vivo, respectively. CONCLUSION: Inner Volume Imaging with 3D‐FSE has been demonstrated in vivo with tailored 3D‐RF pulses. The proposed design methods are also applicable to 2D pulses. Magn Reson Med 76:848–861, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicin

Phase History Decomposition for Efficient Scatterer Classification in SAR Imagery

A new theory and algorithm for scatterer classification in SAR imagery is presented. The automated classification process is operationally efficient compared to existing image segmentation methods requiring human supervision. The algorithm reconstructs coarse resolution subimages from subdomains of the SAR phase history. It analyzes local peaks in the subimages to determine locations and geometric shapes of scatterers in the scene. Scatterer locations are indicated by the presence of a stable peak in all subimages for a given subaperture, while scatterer shapes are indicated by changes in pixel intensity. A new multi-peak model is developed from physical models of electromagnetic scattering to predict how pixel intensities behave for different scatterer shapes. The algorithm uses a least squares classifier to match observed pixel behavior to the model. Classification accuracy improves with increasing fractional bandwidth and is subject to the high-frequency and wide-aperture approximations of the multi-peak model. For superior computational efficiency, an integrated fast SAR imaging technique is developed to combine the coarse resolution subimages into a final SAR image having fine resolution. Finally, classification results are overlaid on the SAR image so that analysts can deduce the significance of the scatterer shape information within the image context

Investigation of Non-coherent Discrete Target Range Estimation Techniques for High-precision Location

Ranging is an essential and crucial task for radar systems. How to solve the range-detection problem effectively and precisely is massively important. Meanwhile, unambiguity and high resolution are the points of interest as well. Coherent and non-coherent techniques can be applied to achieve range estimation, and both of them have advantages and disadvantages. Coherent estimates offer higher precision but are more vulnerable to noise and clutter and phase wrap errors, particularly in a complex or harsh environment, while the non-coherent approaches are simpler but provide lower precision. With the purpose of mitigating inaccuracy and perturbation in range estimation, miscellaneous techniques are employed to achieve optimally precise detection. Numerous elegant processing solutions stemming from non-coherent estimate are now introduced into the coherent realm, and vice versa. This thesis describes two non-coherent ranging estimate techniques with novel algorithms to mitigate the instinct deficit of non-coherent ranging approaches. One technique is based on peak detection and realised by Kth-order Polynomial Interpolation, while another is based on Z-transform and realised by Most-likelihood Chirp Z-transform. A two-stage approach for the fine ranging estimate is applied to the Discrete Fourier transform domain of both algorithms. An N-point Discrete Fourier transform is implemented to attain a coarse estimation; an accurate process around the point of interest determined in the first stage is conducted. For KPI technique, it interpolates around the peak of Discrete Fourier transform profiles of the chirp signal to achieve accurate interpolation and optimum precision. For Most-likelihood Chirp Z-transform technique, the Chirp Z-transform accurately implements the periodogram where only a narrow band spectrum is processed. Furthermore, the concept of most-likelihood estimator is introduced to combine with Chirp Z-transform to acquire better ranging performance. Cramer-Rao lower bound is presented to evaluate the performance of these two techniques from the perspective of statistical signal processing. Mathematical derivation, simulation modelling, theoretical analysis and experimental validation are conducted to assess technique performance. Further research will be pushed forward to algorithm optimisation and system development of a location system using non-coherent techniques and make a comparison to a coherent approach

Time Series Analysis of Surface Deformation Associated With Fluid Injection and Induced Seismicity in Timpson, Texas Using DInSAR Methods

In recent years, a rise in unconventional oil and gas production in North America has been linked to an increase in seismicity rate in these regions (Ellsworth, 2013). As fluid is pumped into deep formations, the state of stress within the subsurface changes, potentially reactivating pre-existing faults and/or causing subsidence or uplift of the surface. Therefore, hydraulic fracturing and/or fluid disposal injection can significantly increase the seismic hazard to communities and structures surrounding the injection sites (Barnhart et al., 2014). On 17th May 2012 an Mw4.8 earthquake occurred near Timpson, TX and has been linked with wastewater injection operations in the area (Shirzaei et al., 2016). This study aims to spatiotemporally relate, wastewater injection operations to seismicity near Timpson using differential interferometric synthetic aperture radar (DInSAR) analysis. Results are presented as a set of time series, produced using the Multidimensional Small Baseline Subset (MSBAS) InSAR technique, revealing two-dimensional surface deformation

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

Quantifying Oil and Gas Industry Related Geohazard Using Radar Interferometry and Hydro-geomechanical Modeling

The Permian Basin, containing a large amount of oil and gas, has been intensively developed for hydrocarbon production. However, the hazards related to the oil and gas industry including surface deformation and the underlying mechanisms in this region have not been well known. My PhD study aims to monitor the geohazards in the Permian Basin and better comprehend the subsurface mechanisms with the aid of high-resolution and high-accuracy Interferometric Synthetic Aperture Radar (InSAR) images. Generally, as the pore pressure is influenced by wastewater injection/hydrocarbon production, the pressure changes can propagate to other surrounding underground and overlying rock/soil layers, resulting in surface deformation. The distribution and temporal development of the surface deformation can be obtained from InSAR processing and analysis. To reveal the underground geo-mechanical process responsible for the development of the surface deformation, numerical modeling based on poroelasticity is then applied to estimate the effective parameters (i.e., parameters inferred from the simulation) including depth and volume. This method is applied to three cases in West Texas. At a site in Reeves county, InSAR detects surface uplift up to 17 cm near a wastewater disposal well from 2007 to 2011. Results from both elastic and poroelastic models indicate that the effective injection depth is much shallower than reported. The most reasonable explanation is that the well was experiencing leakage due to casing failures and/or sealing problem(s). At a site in Winkler county, surface uplift and the follow-on recovery detected by InSAR from 2015 to 2020 can be attributed to nearby wastewater disposal. Bayesian inversion with the poroelastic models provides estimates of the local hydro-geomechanical parameters. The posterior distribution of subsurface effective volumes reveals under-reported volumes in the well near the deformation center. We also investigate a case of aseismic slip related to oil and gas activities. The combination of InSAR observation and poroelastic finite element models in three cases shows the capability to investigate the ongoing geohazards related to fluid injection and hydrocarbon production in the Permian Basin. This kind of study will be helpful to the decision-making of federal/local authorities to avoid future geohazards related to oil and gas activities

Phase error estimation for synthetic aperture imagery.

The estimation of phase errors in synthetic aperture imagery is important for high quality images. Many methods of autofocus, or the estimation of phase errors from the measured data, are developed using certain assumptions about the imaged scene. This thesis develops improved methods of phase estimation which make full use of the information in the recorded signal. This results in both a more accurate estimate of the image phase error and improved imagery compared to using standard techniques. The standard phase estimation kernel used in echo-correlation techniques is shear-average. This technique averages the phase-difference between each ping over all range-bins, weighted by the signal strength. It is shown in this thesis that this is not the optimal method of weighting each phase estimate. In images where the signal to clutter ratio (SCR) is not proportional to the signal amplitude, shear-average does not meet the predicted error bound. This condition may be met by many image types, including those with shadows, distributed targets and varying surface structure. By measuring the average coherence between echos at each range-bin, it is possible to accurately estimate the variance of each phase estimate, and weight accordingly. A weighted phase-difference estimation (WPDE) using this coherence weighting meets the performance bound for all images tested. Thus an improved performance over shear-average is shown for many image types. The WPDE phase estimation method can be used within the framework of many echo-correlation techniques, such as phase-gradient autofocus (PGA), phase curvature estimation, redundant phase-centre or displaced phase-centre algorithms. In addition, a direct centre-shifting method is developed which reduces bias compared to the centre-shifting method used in PGA. For stripmap images, a weighted phase curvature estimator shows better performance than amplitude weighted shear-average for images with high SCR. A different method of phase estimation, known as sharpness maximisation, perturbs an estimate of the phase error to maximise the sharpness of the reconstructed image. Several improvements are made to the technique of sharpness maximisation. These include the reduction of over-sharpening using regularisation and an improvement in accuracy of the phase estimate using range-weighting based on the coherence measure. A cascaded parametric optimisation method is developed which converges significantly faster than standard optimisation methods for stripmap images. A number of novel insights into the method of sharpness maximisation are presented. A derivation of the phase that gives maximum intensity squared sharpness is extended from a noncoherent imaging system to a coherent spotlight system. A bound on the performance of sharpness-maximisation is presented. A method is developed which allows the direct calculation of the result of a sharpness maximisation for a single ping of a spotlight synthetic aperture image. The phase correction that maximises sharpness can be directly calculated from the signal in a manner similar to a high-order echo-correlation. This calculation can be made for all pings in a recursive manner. No optimisation is required, resulting in a significantly faster phase estimation. The techniques of sharpness maximisation and echo-correlation can be shown to be closely related. This is confirmed by direct comparisons of the results. However, the classical intensity-squared sharpness measure gives poorer results than WPDE and different sharpness measures tested for a distributed target. The standard methods of shear average and maximisation of the intensity-squared sharpness measure, both perform well below the theoretical performance bound. Two of the techniques developed, WPDE and direct entropy minimisation perform at the bound, showing improved performance over standard techniques. The contributions of this thesis add considerably to the body of knowledge on the technique of sharpness maximisation. This allows an improvement in the accuracy of some phase estimation methods, as well as an increase in the understanding of how these techniques work on coherent imagery in general