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

Biomass Representation in Synthetic Aperture Radar Interferometry Data Sets

This work makes an attempt to explain the origin, features and potential applications of the elevation bias of the synthetic aperture radar interferometry (InSAR) datasets over areas covered by vegetation. The rapid development of radar-based remote sensing methods, such as synthetic aperture radar (SAR) and InSAR, has provided an alternative to the photogrammetry and LiDAR for determining the third dimension of topographic surfaces. The InSAR method has proved to be so effective and productive that it allowed, within eleven days of the space shuttle mission, for acquisition of data to develop a three-dimensional model of almost the entire land surface of our planet. This mission is known as the Shuttle Radar Topography Mission (SRTM). Scientists across the geosciences were able to access the great benefits of uniformity, high resolution and the most precise digital elevation model (DEM) of the Earth like never before for their a wide variety of scientific and practical inquiries. Unfortunately, InSAR elevations misrepresent the surface of the Earth in places where there is substantial vegetation cover. This is a systematic error of unknown, yet limited (by the vertical extension of vegetation) magnitude. Up to now, only a limited number of attempts to model this error source have been made. However, none offer a robust remedy, but rather partial or case-based solutions. More work in this area of research is needed as the number of airborne and space-based InSAR elevation models has been steadily increasing over the last few years, despite strong competition from LiDAR and optical methods. From another perspective, however, this elevation bias, termed here as the “biomass impenetrability”, creates a great opportunity to learn about the biomass. This may be achieved due to the fact that the impenetrability can be considered a collective response to a few factors originating in 3D space that encompass the outermost boundaries of vegetation. The biomass, presence in InSAR datasets or simply the biomass impenetrability, is the focus of this research. The report, presented in a sequence of sections, gradually introduces terminology, physical and mathematical fundamentals commonly used in describing the propagation of electromagnetic waves, including the Maxwell equations. The synthetic aperture radar (SAR) and InSAR as active remote sensing methods are summarised. In subsequent steps, the major InSAR data sources and data acquisition systems, past and present, are outlined. Various examples of the InSAR datasets, including the SRTM C- and X-band elevation products and INTERMAP Inc. IFSAR digital terrain/surface models (DTM/DSM), representing diverse test sites in the world are used to demonstrate the presence and/or magnitude of the biomass impenetrability in the context of different types of vegetation – usually forest. Also, results of investigations carried out by selected researchers on the elevation bias in InSAR datasets and their attempts at mathematical modelling are reviewed. In recent years, a few researchers have suggested that the magnitude of the biomass impenetrability is linked to gaps in the vegetation cover. Based on these hints, a mathematical model of the tree and the forest has been developed. Three types of gaps were identified; gaps in the landscape-scale forest areas (Type 1), e.g. forest fire scares and logging areas; a gap between three trees forming a triangle (Type 2), e.g. depending on the shape of tree crowns; and gaps within a tree itself (Type 3). Experiments have demonstrated that Type 1 gaps follow the power-law density distribution function. One of the most useful features of the power-law distributed phenomena is their scale-independent property. This property was also used to model Type 3 gaps (within the tree crown) by assuming that these gaps follow the same distribution as the Type 1 gaps. A hypothesis was formulated regarding the penetration depth of the radar waves within the canopy. It claims that the depth of penetration is simply related to the quantisation level of the radar backscattered signal. A higher level of bits per pixels allows for capturing weaker signals arriving from the lower levels of the tree crown. Assuming certain generic and simplified shapes of tree crowns including cone, paraboloid, sphere and spherical cap, it was possible to model analytically Type 2 gaps. The Monte Carlo simulation method was used to investigate relationships between the impenetrability and various configurations of a modelled forest. One of the most important findings is that impenetrability is largely explainable by the gaps between trees. A much less important role is played by the penetrability into the crown cover. Another important finding is that the impenetrability strongly correlates with the vegetation density. Using this feature, a method for vegetation density mapping called the mean maximum impenetrability (MMI) method is proposed. Unlike the traditional methods of forest inventories, the MMI method allows for a much more realistic inventory of vegetation cover, because it is able to capture an in situ or current situation on the ground, but not for areas that are nominally classified as a “forest-to-be”. The MMI method also allows for the mapping of landscape variation in the forest or vegetation density, which is a novel and exciting feature of the new 3D remote sensing (3DRS) technique. Besides the inventory-type applications, the MMI method can be used as a forest change detection method. For maximum effectiveness of the MMI method, an object-based change detection approach is preferred. A minimum requirement for the MMI method is a time-lapsed reference dataset in the form, for example, of an existing forest map of the area of interest, or a vegetation density map prepared using InSAR datasets. Preliminary tests aimed at finding a degree of correlation between the impenetrability and other types of passive and active remote sensing data sources, including TerraSAR-X, NDVI and PALSAR, proved that the method most sensitive to vegetation density was the Japanese PALSAR - L-band SAR system. Unfortunately, PALSAR backscattered signals become very noisy for impenetrability below 15 m. This means that PALSAR has severe limitations for low loadings of the biomass per unit area. The proposed applications of the InSAR data will remain indispensable wherever cloud cover obscures the sky in a persistent manner, which makes suitable optical data acquisition extremely time-consuming or nearly impossible. A limitation of the MMI method is due to the fact that the impenetrability is calculated using a reference DTM, which must be available beforehand. In many countries around the world, appropriate quality DTMs are still unavailable. A possible solution to this obstacle is to use a DEM that was derived using P-band InSAR elevations or LiDAR. It must be noted, however, that in many cases, two InSAR datasets separated by time of the same area are sufficient for forest change detection or similar applications

The development of a 3-Pass Persistent Scatterer algorithm using the Integer Ambiguity Search method

Differential interferometric synthetic aperture radar (InSAR) is a well used technique for measuring deformations, but often suffers greatly from effects due to atmospheric differences occurring between different SAR images. Recently a new set of techniques have been developed called Persistent Scatterer techniques, which take advantage of the high number of SAR images available to try and model out the atmospheric effects. To aid this process, all present techniques make use of a Digital Elevation Model (DEM) to remove the interferometric phase due to topography, but as a consequence this contaminates the phase values with an unknown error due to the DEM, which has to be modelled out in the processing chain. In this thesis a new Persistent Scatterer (PS) method is proposed that does not use a DEM to remove the topographic phase component, but rather one of the interferograms used in the study, and hence does not need to calculate topographic height corrections. This is achieved by using the Integer Ambiguity Search (IAS) 3-Pass differential technique. The developed algorithms are then tested and assessed over two test sites in central London, UK. The overall conclusions of the research are summarised below. The IAS 3-pass differential interferometry method gives a differential result that is more consistent with 2-pass results than with traditional 3-pass results. By using the IAS 3-pass method, it is possible to do a PS InSAR analysis without recourse to a DEM or needing to derive any topographic information. The developed IAS PSInSAR algorithms have been tested using simulated data, which has shown that the methods developed can identify small scale target motion over a period of a few years. The IAS PSInSAR algorithms were also tested using real SAR data, the results of which are consistent with GPS results of the test site and previous independent investigations

Electronic countermeasures applied to passive radar

Passive Radar (PR) is a form of bistatic radar that utilises existing transmitter infrastructure such as FM radio, digital audio and video broadcasts (DAB and DVB-T/T2), cellular base station transmitters, and satellite-borne illuminators like DVB-S instead of a dedicated radar transmitter. Extensive research into PR has been performed over the last two decades across various industries with the technology maturing to a point where it is becoming commercially viable. Nevertheless, despite the abundance of PR literature, there is a scarcity of open literature pertaining to electronic countermeasures (ECM) applied to PR. This research makes the novel contribution of a comprehensive exploration and validation of various ECM techniques and their effectiveness when applied to PR. Extensive research has been conducted to assess the inherent properties of the lluminators of Opportunity to identify their possible weaknesses for the purpose of applying targeted ECM. Similarly, potential jamming signals have also been researched to evaluate their effectiveness as bespoke ECM signals. Whilst different types of PR exist, this thesis focuses specifically on ECM applied to FM radio and DVB-T2 based PR. The results show noise jamming to be effective against FM radio based PR where jamming can be achieved with relatively low jamming power. A waveform study is performed to determine the optimal jamming waveform for an FM radio based PR. The importance of an effective direct signal interference (DSI) canceller is also shown as a means of suppressing the jamming signal. A basic overview of counter-ECM (ECCM) is discussed to counter potential jamming of FM based PR. The two main processing techniques for DVB-T2 based PR, mismatched and inverse filtering, have been investigated and their performance in the presence of jamming evaluated. The deterministic components of the DVB-T2 waveform are shown to be an effective form of attack for both mismatched filtering and inverse filtering techniques. Basic ECCM is also presented to counter potential pilot attacks on DVB-T2 based PR. Using measured data from a PR demonstrator, the application and effectiveness of each jamming technique is clearly demonstrated, evaluated and quantified

Aeronautical Engineering: A special bibliography with indexes, supplement 55

This bibliography lists 260 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1975

Modeling and performance analysis of a UAV-based sensor network for improved ATR

Automatic Target Recognition (ATR) is computer processing of images or signals acquired by sensors with the purpose to identify objects of interest (targets). This technology is a critical element for surveillance missions. Over the past several years there has been an increasing trend towards fielding swarms of unattended aerial vehicles (UAVs) operating as sensor networks in the air. This trend offers opportunities of integration ATR systems with a UAV-based sensor network to improve the recognition performance. This dissertation addresses some of design issues of ATR systems, explores recognition capabilities of sensor networks in the presence of various distortions and analyzes the limiting recognition performance of sensor networks.;We assume that each UAV is equipped with an optical camera. A model based recognition method for single and multiple frames is introduced. A complete ATR system, including detection, segmentation, recognition and clutter rejection, is designed and tested using synthetic and realistic images. The effects of environmental conditions on target recognition are also investigated.;To analyze and predict ATR performance of a recognition sensor network, a general methodology from information theory view point is used. Given the encoding method, the recognition system is analyzed using a recognition channel. The concepts of recognition capacity, error exponents and probability of outage are defined and derived for a PCA-based ATR system. Both the case of a single encoded image and the case of encoded correlated multiple frames are analyzed. Numerical evaluations are performed. Finally we discuss the joint recognition and communication problems. Three scenarios of a two node recognition sensor network are analyzed. The communication and recognition performances for each scenario are evaluated numerically

NASA thesaurus. Volume 3: Definitions

Publication of NASA Thesaurus definitions began with Supplement 1 to the 1985 NASA Thesaurus. The definitions given here represent the complete file of over 3,200 definitions, complimented by nearly 1,000 use references. Definitions of more common or general scientific terms are given a NASA slant if one exists. Certain terms are not defined as a matter of policy: common names, chemical elements, specific models of computers, and nontechnical terms. The NASA Thesaurus predates by a number of years the systematic effort to define terms, therefore not all Thesaurus terms have been defined. Nevertheless, definitions of older terms are continually being added. The following data are provided for each entry: term in uppercase/lowercase form, definition, source, and year the term (not the definition) was added to the NASA Thesaurus. The NASA History Office is the authority for capitalization in satellite and spacecraft names. Definitions with no source given were constructed by lexicographers at the NASA Scientific and Technical Information (STI) Facility who rely on the following sources for their information: experts in the field, literature searches from the NASA STI database, and specialized references

Multi-source Remote Sensing for Forest Characterization and Monitoring

As a dominant terrestrial ecosystem of the Earth, forest environments play profound roles in ecology, biodiversity, resource utilization, and management, which highlights the significance of forest characterization and monitoring. Some forest parameters can help track climate change and quantify the global carbon cycle and therefore attract growing attention from various research communities. Compared with traditional in-situ methods with expensive and time-consuming field works involved, airborne and spaceborne remote sensors collect cost-efficient and consistent observations at global or regional scales and have been proven to be an effective way for forest monitoring. With the looming paradigm shift toward data-intensive science and the development of remote sensors, remote sensing data with higher resolution and diversity have been the mainstream in data analysis and processing. However, significant heterogeneities in the multi-source remote sensing data largely restrain its forest applications urging the research community to come up with effective synergistic strategies. The work presented in this thesis contributes to the field by exploring the potential of the Synthetic Aperture Radar (SAR), SAR Polarimetry (PolSAR), SAR Interferometry (InSAR), Polarimetric SAR Interferometry (PolInSAR), Light Detection and Ranging (LiDAR), and multispectral remote sensing in forest characterization and monitoring from three main aspects including forest height estimation, active fire detection, and burned area mapping. First, the forest height inversion is demonstrated using airborne L-band dual-baseline repeat-pass PolInSAR data based on modified versions of the Random Motion over Ground (RMoG) model, where the scattering attenuation and wind-derived random motion are described in conditions of homogeneous and heterogeneous volume layer, respectively. A boreal and a tropical forest test site are involved in the experiment to explore the flexibility of different models over different forest types and based on that, a leveraging strategy is proposed to boost the accuracy of forest height estimation. The accuracy of the model-based forest height inversion is limited by the discrepancy between the theoretical models and actual scenarios and exhibits a strong dependency on the system and scenario parameters. Hence, high vertical accuracy LiDAR samples are employed to assist the PolInSAR-based forest height estimation. This multi-source forest height estimation is reformulated as a pan-sharpening task aiming to generate forest heights with high spatial resolution and vertical accuracy based on the synergy of the sparse LiDAR-derived heights and the information embedded in the PolInSAR data. This process is realized by a specifically designed generative adversarial network (GAN) allowing high accuracy forest height estimation less limited by theoretical models and system parameters. Related experiments are carried out over a boreal and a tropical forest to validate the flexibility of the method. An automated active fire detection framework is proposed for the medium resolution multispectral remote sensing data. The basic part of this framework is a deep-learning-based semantic segmentation model specifically designed for active fire detection. A dataset is constructed with open-access Sentinel-2 imagery for the training and testing of the deep-learning model. The developed framework allows an automated Sentinel-2 data download, processing, and generation of the active fire detection results through time and location information provided by the user. Related performance is evaluated in terms of detection accuracy and processing efficiency. The last part of this thesis explored whether the coarse burned area products can be further improved through the synergy of multispectral, SAR, and InSAR features with higher spatial resolutions. A Siamese Self-Attention (SSA) classification is proposed for the multi-sensor burned area mapping and a multi-source dataset is constructed at the object level for the training and testing. Results are analyzed by different test sites, feature sources, and classification methods to assess the improvements achieved by the proposed method. All developed methods are validated with extensive processing of multi-source data acquired by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), Land, Vegetation, and Ice Sensor (LVIS), PolSARproSim+, Sentinel-1, and Sentinel-2. I hope these studies constitute a substantial contribution to the forest applications of multi-source remote sensing