Enhanced Polarimetric Radar Imaging Using Cross-Channel Coupling Constraints

Data for a scene of interest may be collected over multiple polarization channels. In the case of polarimetric synthetic aperture radar images, regularization techniques are typically applied independently to each polarimetric channel. However, independent processing does not account for cross-channel coupling and may corrupt the polarimetric information in the signals. Recent consideration of joint enhancement techniques has shown promising results for multi- channel datasets with similar regions of signal magnitude and/or phase. However, in the case of polarimetric SAR data, scattering may be present in some channels and not in others. This thesis mathematically formulates multi-channel sparse imaging for polarimetric radar data using a joint enhancement algorithm to enforce sparsity and polarimetric coupling constraints. Two candidate functional relationships are derived to describe polarimetric coupling among received signal channels: one convex function and one non-convex function. These functions are reformed as optimization constraints. Then, an optimization problem is constructed to maintain signal fidelity, enforce sparsity, and preserve interchannel coupling. An iterative dual gradient descent algorithm is used to alternatively calculate updated scene estimates for each channel and the maximizing Lagrange multipliers for each coupling constraint. Results are found for several polarimetric SAR datasets. Jointly enhanced images are compared with corresponding images found through independent enhancement, taking into consideration signal fidelity, sparsity, polarimetric preservation, and scattering classification. Overall, the jointly enhanced image channels display significantly better polarimetric preservation compared to the corresponding independently restored image channels. More research is needed to understand how improved polarimetric preservation can be used to improve target classification

Recent Techniques for Regularization in Partial Differential Equations and Imaging

abstract: Inverse problems model real world phenomena from data, where the data are often noisy and models contain errors. This leads to instabilities, multiple solution vectors and thus ill-posedness. To solve ill-posed inverse problems, regularization is typically used as a penalty function to induce stability and allow for the incorporation of a priori information about the desired solution. In this thesis, high order regularization techniques are developed for image and function reconstruction from noisy or misleading data. Specifically the incorporation of the Polynomial Annihilation operator allows for the accurate exploitation of the sparse representation of each function in the edge domain. This dissertation tackles three main problems through the development of novel reconstruction techniques: (i) reconstructing one and two dimensional functions from multiple measurement vectors using variance based joint sparsity when a subset of the measurements contain false and/or misleading information, (ii) approximating discontinuous solutions to hyperbolic partial differential equations by enhancing typical solvers with l1 regularization, and (iii) reducing model assumptions in synthetic aperture radar image formation, specifically for the purpose of speckle reduction and phase error correction. While the common thread tying these problems together is the use of high order regularization, the defining characteristics of each of these problems create unique challenges. Fast and robust numerical algorithms are also developed so that these problems can be solved efficiently without requiring fine tuning of parameters. Indeed, the numerical experiments presented in this dissertation strongly suggest that the new methodology provides more accurate and robust solutions to a variety of ill-posed inverse problems.Dissertation/ThesisDoctoral Dissertation Mathematics 201

Convex Model-Based Synthetic Aperture Radar Processing

The use of radar often conjures up images of small blobs on a screen. But current synthetic aperture radar (SAR) systems are able to generate near-optical quality images with amazing benefits compared to optical sensors. These SAR sensors work in all weather conditions, day or night, and provide many advanced capabilities to detect and identify targets of interest. These amazing abilities have made SAR sensors a work-horse in remote sensing, and military applications. SAR sensors are ranging instruments that operate in a 3D environment, but unfortunately the results and interpretation of SAR images have traditionally been done in 2D. Three-dimensional SAR images could provide improved target detection and identification along with improved scene interpretability. As technology has increased, particularly regarding our ability to solve difficult optimization problems, the 3D SAR reconstruction problem has gathered more interest. This dissertation provides the SAR and mathematical background required to pose a SAR 3D reconstruction problem. The problem is posed in a way that allows prior knowledge about the target of interest to be integrated into the optimization problem when known. The developed model is demonstrated on simulated data initially in order to illustrate critical concepts in the development. Then once comprehension is achieved the processing is applied to actual SAR data. The 3D results are contrasted against the current gold- standard. The results are shown as 3D images demonstrating the improvement regarding scene interpretability that this approach provides

Space-time sampling strategies for electronically steerable incoherent scatter radar

Incoherent scatter radar (ISR) systems allow researchers to peer into the ionosphere via remote sensing of intrinsic plasma parameters. ISR sensors have been used since the 1950s and until the past decade were mainly equipped with a single mechanically steerable antenna. As such, the ability to develop a two or three dimensional picture of the plasma parameters in the ionosphere has been constrained by the relatively slow mechanical steering of the antennas. A newer class of systems using electronically steerable array (ESA) antennas have broken the chains of this constraint, allowing researchers to create 3-D reconstructions of plasma parameters. There have been many studies associated with reconstructing 3-D fields of plasma parameters, but there has not been a systematic analysis into the sampling issues that arise. Also, there has not been a systematic study as to how to reconstruct these plasma parameters in an optimum sense as opposed to just using different forms of interpolation. The research presented here forms a framework that scientists and engineers can use to plan experiments with ESA ISR capabilities and to better analyze the resulting data. This framework attacks the problem of space-time sampling by ESA ISR systems from the point of view of signal processing, simulation and inverse theoretic image reconstruction. We first describe a physics based model of incoherent scatter from the ionospheric plasma, along with processing methods needed to create the plasma parameter measurements. Our approach leads to development of the space-time ambiguity function, forming a theoretical foundation of the forward model for ISR. This forward model is novel in that it takes into account the shape of the antenna beam and scanning method along with integration time to develop the proper statistics for a desired measurement precision. Once the forward model is developed, we present the simulation method behind the Simulator for ISR (SimISR). SimISR uses input plasma parameters over space and time and creates complex voltage samples in a form similar to that produced by a real ISR system. SimISR allows researchers to evaluate different experiment configurations in order to efficiently and accurately sample specific phenomena. We present example simulations using input conditions derived from a multi-fluid ionosphere model and reconstructions using standard interpolation techniques. Lastly, methods are presented to invert the space-time ambiguity function using techniques from image reconstruction literature. These methods are tested using SimISR to quantify accurate plasma parameter reconstruction over a simulated ionospheric region

Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

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SAR imaging of moving targets by subaperture based low-rank and sparse decomposition

Synthetic aperture radar (SAR) has gained significance as an indispensable instrument of remote sensing and airborne surveillance. Its applications extend to 3D terrain mapping, oil spill detection, crop yield estimation and disaster evaluation. SAR utilizes platform motion to synthesize a large antenna thus rendering a very fine spatial resolution. Nevertheless, imaging of moving targets with SAR is a challenging problem. In this thesis, we propose a moving target imaging approach for SAR which exploits the low-rank and sparse decomposition (LRSD) of the subaperture data. As a first step, multiple subapertures are constructed from the raw data using frequency domain filtering. In contrast to the stationary points, moving targets in the SAR scene shift their position in the various subapertures. This enables a successful low-rank and sparse decomposition of the subaperture data where the sparse component captures the moving targets’ phase histories and reflectivity profiles. On the other hand, the low-rank component consists of the static background due to fewer spatial variations in multiple subapertures. This framework allows the reconstruction of full-resolution sparse and low-rank images by combining the spectral information of the decomposed subapertures. Furthermore, it enhances the applicability of sparsity-driven moving target imaging frameworks to very low signal to clutter ratio (SCR) scenarios by offering a considerable SCR performance improvement. We manifest the effectiveness of our approach through experiments with synthetic as well as real SAR data. Our real SAR experiments were based on MiniSAR and EMISAR data

Deep learning for inverse problems in remote sensing: super-resolution and SAR despeckling

L'abstract è presente nell'allegato / the abstract is in the attachmen

Advanced Techniques for Ground Penetrating Radar Imaging

Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives

Studies of global cloud field using measurements of GOME, SCIAMACHY and GOME-2

Tropospheric clouds are main players in the Earth climate system. Characterization of long-term global and regional cloud properties aims to support trace-gases retrieval, radiative budget assessment, and analysis of interactions with particles in the atmosphere. The information needed for the determination of cloud properties can be optimally obtained with satellite remote sensing systems. This is because the amount of reflected solar light depends both on macro- and micro-physical characteristics of clouds. At the time of writing, the spaceborne nadir-viewing Global Ozone Monitoring Experiment (GOME), together with the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and GOME-2, make available a unique record of almost 17 years (June 1996 throughout May 2012) of global top-of-atmosphere (TOA) reflectances and form the observational basis of this work. They probe the atmosphere in the ultraviolet, visible and infrared regions of the electromagnetic spectrum. Specifically, in order to infer cloud properties such as optical thickness (COT), spherical albedo (CA), cloud base (CBH) and cloud top (CTH) height, TOA reflectances have been selected inside and around the strong absorption band of molecular oxygen in the wavelength range at 758-772 nm (the O2 A-band). The retrieval is accomplished using the Semi-Analytical CloUd Retrieval Algorithm (SACURA). The physical framework relies on the asymptotic parameterizations of radiative transfer. The generated record has been throughly verified against synthetic datasets as function of cloud and surface parameters, sensing geometries, and instrumental specifications and validated against ground-based retrievals. The error budget analysis shows that SACURA retrieves CTH with an average accuracy of ±400 m, COT within ±20% (given that COT > 5) and places CTH closer to ground-based radar-derived CTH, as compared to independent satellite-based retrievals. In the considered time period the global average CTH is 5.2±3.0 km, for a corresponding average COT of 20.5±16.1 and CA of 0.62±0.11. Using linear least-squares techniques, global trend in deseasonalized CTH has been found to be -1.78±2.14 m * year-1 in the latitude belt ±60°, with diverging tendency over land ( 0.27±3.2 m * year-1) and water (-2.51±2.8 m * year-1) masses. The El Nino-Southern Oscillation (ENSO), observed through CTH and cloud fraction (CF) values over the Pacific Ocean, pulls clouds to lower altitudes. It is argued that ENSO must be removed for trend analysis. The global ENSO-cleaned trend in CTH amounts to -0.49±2.22 m * year-1. At a global scale, no explicit patterns of statistically significant trends (at 95% confidence level, estimated with bootstrap resampling technique) have been found, which are representative of peculiar natural climate variability. One exception is the Sahara region, which exhibits the strongest upward trend in CTH, sustained by an increasing trend in water vapor. Indeed, the representativeness of every trend is affected by the record length under study. 17 years of cloud data still might not be enough to provide any decisive answer to current open questions involving clouds. The algorithm used in this work can be applied to measurements provided by future planned Earth's observation missions. In this way, the existing cloud record will be extended and attribution of cloud property changes to natural or human causes and assessment of cloud feedback sign within the climate system can be investigated