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

Visualisation of polarimetric radar data

This thesis examines the application of scientific visualisation to the analysis of polarimetric radar data sets. The research contained herein forms part of a larger body of work that studies the application of scientific visualisation to the analysis of large multi-valued datasets. Visualisation techniques have historically assumed a fundamental role in the analysis of patterns in geographic datasets. This is particularly apparent in the analysis of remotely sensed data, which, since the advent of aerial photography, has utilised the intensity of visible (and invisible) electromagnetic energy as a means of producing synoptic map-like images. Progress in remote sensing technology, however, has led to the development of systems which measure very large numbers of intensity 'channels', or require the analysis of variables other than intensity values. Current visualisation strategies are insufficient to adequately represent such datasets, whilst retaining the synoptic perspective. In response to this, two new visualisation techniques are presented for the analysis of polarimetric radar data. Both techniques demonstrate how it is possible to produce synoptic image suitable for the analysis of spatial patterns without relying on pixel based intensity images. This allows a large number of variables to be ascribed to a single geographic location, and thus encourages the rapid identification of patterns and anomalies within datasets. The value of applying the principals of scientific visualisation to exploratory data analysis is subsequently demonstrated with reference to a number of case studies that highlight the potential of the newly developed techniques

Modeling the Radar Return of Powerlines Using an Incremental Length Diffraction Coefficient Approach

A method for modeling the signal from cables and powerlines in Synthetic Aperture Radar (SAR) imagery is presented. Powerline detection using radar is an active area of research. Accurately identifing the location of powerlines in a scene can be used to aid pilots of low flying aircraft in collision avoidance, or map the electrical infrastructure of an area. The focus of this research was on the forward modeling problem of generating the powerline SAR signal from first principles. Previous work on simulating SAR imagery involved methods that ranged from efficient but insufficiently accurate, depending on the application, to more exact but computationally complex. A brief survey of the numerous ways to model the scattering of electromagnetic radiation is provided. A popular tool that uses the geometric optics approximation for modeling imagery for remote sensing applications across a wide range of modalities is the Digitial Imaging and Remote Sensing Image Generation (DIRSIG) tool. This research shows the way in which DIRSIG generates the SAR phase history is unique compared to other methods used. In particular, DIRSIG uses the geometric optics approximation for the scattering of electromagnetic radiation and builds the phase history in the time domain on a pulse-by-pulse basis. This enables an efficient generation of the phase history of complex scenes. The drawback to this method is the inability to account for diffraction. Since the characteristic diameter of many communication cables and powerlines is on the order of the wavelength of the incident radiation, diffraction is the dominant mechanism by which the radiation gets scattered for these targets. Comparison of DIRSIG imagery to field data shows good scene-wide qualitative agreement as well as Rayleigh distributed noise in the amplitude data, as expected for coherent imaging with speckle. A closer inspection of the Radar Cross Sections of canonical targets such as trihedrals and dihedrals, however, shows DIRSIG consistently underestimated the scattered return, especially away from specular observation angles. This underestimation was particularly pronounced for the dihedral targets which have a low acceptance angle in elevation, probably caused by the lack of a physical optics capability in DIRSIG. Powerlines were not apparent in the simulated data. For modeling powerlines outside of DIRSIG using a standalone approach, an Incremental Length Diffraction Coefficient (ILDC) method was used. Traditionally, this method is used to model the scattered radiation from the edge of a wedge, for example the edges on the wings of a stealth aircraft. The Physical Theory of Diffraction provides the 2D diffraction coefficient and the ILDC method performs an integral along the edge to extend this solution to three dimensions. This research takes the ILDC approach but instead of using the wedge diffraction coefficient, the exact far-field diffraction coefficient for scattering from a finite length cylinder is used. Wavenumber-diameter products are limited to less than or about 10. For typical powerline diameters, this translates to X-band frequencies and lower. The advantage of this method is it allows exact 2D solutions to be extended to powerline geometries where sag is present and it is shown to be more accurate than a pure physical optics approach for frequencies lower than millimeter wave. The Radar Cross Sections produced by this method were accurate to within the experimental uncertainty of measured RF anechoic chamber data for both X and C-band frequencies across an 80 degree arc for 5 different target types and diameters. For the X-band data, the mean error was 6.0% for data with 9.5% measurement uncertainty. For the C-band data, the mean error was 11.8% for data with 14.3% measurement uncertainty. The best results were obtained for X-band data in the HH polarization channel within a 20 degree arc about normal incidence. For this configuration, a mean error of 3.0% for data with a measurement uncertainty of 5.2% was obtained. The least accurate results were obtained for X-band data in the VV polarization channel within a 20 degree arc about normal incidence. For this configuration, a mean error of 8.9% for data with a measurement uncertainty of 5.9% was obtained. This error likely arose from making the smooth cylinder assumption, which neglects the semi-open waveguide TE contribution from the grooves in the helically wound powerline. For field data in an actual X-band circular SAR collection, a mean error of 3.3% for data with a measurement uncertainty of 3.3% was obtained in the HH channel. For the VV channel, a mean error of 9.9% was obtained for data with a measurement uncertainty of 3.4%. Future work for improving this method would likely entail adding a far-field semi-open waveguide contribution to the 2D diffraction coefficient for TE polarized radiation. Accounting for second order diffractions between closely spaced powerlines would also lead to improved accuracy for simulated field data

Enhanced Microwave Imaging of the Subsurface for Humanitarian Demining Applications

© Cranfield University 2020. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright ownerThis thesis presents a theoretical analysis and applied evaluation deploying ground penetrat ing radar (GPR) for landmine detection. An original contribution has been made in designing and manufacturing a light-weight, low-cost, fully polarimetric antenna system for GPR, enabling easy transportation and as sembly. This facilitates extensive use by various smaller communities in remote areas. By achieving the goal of supplying various smaller communities with advanced ground pene trating radar technology the technological standard of landmine detection can be improved beyond existing solutions such as metal detection or manual probing. The novel radar system itself allows detection of various subsurface targets of different shapes and sizes, metallic and non-metallic, in a number of different soils, such as sand, loam or gravel and therefore can be used in versatile environments. The GPR system has been realised by designing novel light-weight, 3D printed X-band horn antennas, manufactured from single piece plastic then copper electroplated. These an tennas are 50% lighter than their commercial equivalents. They are incorporated in an an tenna array as a group of four to allow full-polarimetric imaging of the subsurface. High resolution images of landmines and calibration targets were performed in the subsurface over an experimental sand test bed. For performing subsurface measurements in the near-field, four novel gradient-index (GRIN) lenses were designed and 3D printed to be incorporated in the apertures of the X band antennas. The improved target detection from these lenses was proven by scanning the test bed and comparing the imaging data of the antenna array with and without lenses attached. A rigorous theoretical study of different decomposition techniques and their effect on the imaging and detection accuracy for polarimetric surface penetrating data was performed and applied to the gathered imaging data to reliably isolate and detect subsurface targets. Studied decomposition techniques were Pauli decomposition parameters and Yamaguchi polarime try decomposition. It was found that it is paramount to use both algorithms on one set of subsurface data to detect all features of a buried target. A novel temporal imaging technique was developed for exploiting natural occurring changes in soil moisture level, and hence its dielectric properties. Contrary to the previously intro duced imaging techniques this moisture change detection (MCD) mechanism does not rely on knowledge of the used measurement setup or deploying clutter suppression techniques. This time averaged technique uses several images of a moist subsurface taken over a period while the moisture evaporates from the soil. Each image pixel is weighted by the phase change occurring over the evaporation period and a resulting B-scan image reveals the subsurface targets without surrounding clutter. Finally, a multi-static antenna set-up is examined on its capability for suppressing sur face clutter and its limitations are verified by introducing artificial surface clutter in form of pebbles to the scene. The resulting technique was found to suppress up to 30 The GPR antenna system developed in this thesis and the corresponding imaging tech niques have contributed to a significant improvement in subsurface radar imaging perfor mance and target discrimination capabilities. This work will contribute to more efficient landmine clearance in some of the most challenged parts of the world.Ph

Enhanced microwave imaging of the subsurface for humanitarian demining applications

© Cranfield University 2020. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright ownerThis thesis presents a theoretical analysis and applied evaluation deploying ground penetrating radar (GPR) for landmine detection. An original contribution has been made in designing and manufacturing a light-weight, low-cost, fully polarimetric antenna system for GPR, enabling easy transportation and assembly. This facilitates extensive use by various smaller communities in remote areas. By achieving the goal of supplying various smaller communities with advanced ground penetrating radar technology the technological standard of landmine detection can be improved beyond existing solutions such as metal detection or manual probing. The novel radar system itself allows detection of various subsurface targets of different shapes and sizes, metallic and non-metallic, in a number of different soils, such as sand, loam or gravel and therefore can be used in versatile environments. The GPR system has been realised by designing novel light-weight, 3D printed X-band horn antennas, manufactured from single piece plastic then copper electroplated. These antennas are 50% lighter than their commercial equivalents. They are incorporated in an antenna array as a group of four to allow full-polarimetric imaging of the subsurface. High resolution images of landmines and calibration targets were performed in the subsurface over an experimental sand test bed. For performing subsurface measurements in the near-field, four novel gradient-index (GRIN) lenses were designed and 3D printed to be incorporated in the apertures of the Xband antennas. The improved target detection from these lenses was proven by scanning the test bed and comparing the imaging data of the antenna array with and without lensesattached. A rigorous theoretical study of different decomposition techniques and their effect on the imaging and detection accuracy for polarimetric surface penetrating data was performed and applied to the gathered imaging data to reliably isolate and detect subsurface targets. Studied decomposition techniques were Pauli decomposition parameters and Yamaguchi polarimetry decomposition. It was found that it is paramount to use both algorithms on one set of subsurface data to detect all features of a buried target. A novel temporal imaging technique was developed for exploiting natural occurring changes in soil moisture level, and hence its dielectric properties. Contrary to the previously introduced imaging techniques this moisture change detection (MCD) mechanism does not rely on knowledge of the used measurement setup or deploying clutter suppression techniques. This time averaged technique uses several images of a moist subsurface taken over a period while the moisture evaporates from the soil. Each image pixel is weighted by the phase change occurring over the evaporation period and a resulting B-scan image reveals the subsurface targets without surrounding clutter. Finally, a multi-static antenna set-up is examined on its capability for suppressing surface clutter and its limitations are verified by introducing artificial surface clutter in form of pebbles to the scene. The resulting technique was found to suppress up to 30 The GPR antenna system developed in this thesis and the corresponding imaging techniques have contributed to a significant improvement in subsurface radar imaging performance and target discrimination capabilities. This work will contribute to more efficient landmine clearance in some of the most challenged parts of the world

Polarimetric Radar for Automotive Applications

Current automotive radar sensors prove to be a weather robust and low-cost solution, but are suffering from low resolution and are not capable of classifying detected targets. However, for future applications like autonomous driving, such features are becoming ever increasingly important. On the basis of successful state-of-the-art applications, this work presents the first in-depth analysis and ground-breaking, novel results of polarimetric millimeter wave radars for automotive applications

Quad-Polarimetric Multi-Scale Analysis of Icebergs in ALOS-2 SAR Data: A Comparison between Icebergs in West and East Greenland

Icebergs are ocean hazards which require extensive monitoring. Synthetic Aperture Radar (SAR) satellites can help with this, however, SAR backscattering is strongly influenced by the properties of icebergs, together with meteorological and environmental conditions. In this work, we used five images of quad-pol ALOS-2/PALSAR-2 SAR data to analyse 1332 icebergs in five locations in west and east Greenland. We investigate the backscatter and polarimetric behaviour, by using several observables and decompositions such as the Cloude–Pottier eigenvalue/eigenvector and Yamaguchi model-based decompositions. Our results show that those icebergs can contain a variety of scattering mechanisms at L-band. However, the most common scattering mechanism for icebergs is surface scattering, with the second most dominant volume scattering (or more generally, clouds of dipoles). In some cases, we observed a double bounce dominance, but this is not as common. Interestingly, we identified that different locations (e.g., glaciers) produce icebergs with different polarimetric characteristics. We also performed a multi-scale analysis using boxcar 5 × 5 and 11 × 11 window sizes and this revealed that depending on locations (and therefore, characteristics) icebergs can be a collection of strong scatterers that are packed in a denser or less dense way. This gives hope for using quad-pol polarimetry to provide some iceberg classifications in the future

Investigation of non-cooperative target recognition of small and slow moving air targets in modern air defence surveillance radar

This thesis covers research in the field of non-cooperative target recognition given the limitations of modern air defence surveillance radars. The potential presence of low observable manned or unmanned targets within the vast surveillance volume demand highly sensitive systems. This may again introduce unwanted detections of single birds of comparable radar cross section, previously avoided by use of wide clutter rejection filters and sensitivity time control. The demand for methods effectively separating between birds and slow moving manmade targets is evident. The research questions addressed are connected to identification of characteristic features of birds and manmade targets of comparable size. Ultimately the goal has been to find methods that can utilize such features to effectively distinguish between the classes. In contrast to the vast majority of non-cooperative target recognition publications, this thesis includes non-rigid targets covering a range of dielectric properties and targets falling in the resonant and Rayleigh scattering regions. These factors combined with insufficient spatial resolution for classification require alternative approaches such as utilization of periodic RCS modulation, micro-Doppler- and polarimetric signatures. Signatures of birds and UAVs are investigated through electromagnetic prediction and radar measurements. A flexible and fully polarimetric radar capable of simultaneous operation in both L- and S-band is developed for collection of relevant signatures. Inspired by the use of polarimetric radar for classification of precipitation covered in the weather radar literature, focus has been on using similar methods to recognize signatures of rotors, propellers and bird wings. Novel micro-Doppler signatures combining polarimetric information from this sensor is found to hold information about the orientation of such target parts. This information combined with several other features is evaluated for classification. The benefit from involving polarimetric measurements is especially investigated, and is found to be highly valuable when information provided by other methods is limited

Growing stock volume estimation in temperate forsted areas using a fusion approach with SAR Satellites Imagery

Forest monitoring plays a central role in the context of global warming mitigation and in the assessment of forest resources. To meet these challenges, significant efforts have been made by scientists to develop new feasible remote sensing techniques for the retrieval of forest parameters. However, much work remains to be done in this area, in particular in establishing global assessments of forest biomass. In this context, this Ph.D. Thesis presents a complete methodology for estimating Growing Stock Volume (GSV) in temperate forested areas using a fusion approach based on Synthetic-Aperture Radar (SAR) satellite imagery. The investigations which were performed focused on the Thuringian Forest, which is located in Central Germany. The satellite data used are composed of an extensive set of L-band (ALOS PALSAR) and X-band (TerraSAR-X, TanDEM-X, Cosmo-SkyMed) images, which were acquired in various sensor configurations (acquisition modes, polarisations, incidence angles). The available ground data consists of a forest inventory delivered by the local forest offices. Weather measurements and a LiDAR DEM complete the datasets. The research showed that together with the topography, the forest structure and weather conditions generally limited the sensitivity of the SAR signal to GSV. The best correlations were obtained with ALOS PALSAR (R2 = 0.61) and TanDEM-X (R2 = 0.72) interferometric coherences. These datasets were chosen for the retrieval of GSV in the Thuringian Forest and led with regressions to an root-mean-square error (RMSE) in the range of 100─200 m3ha-1. As a final achievement of this thesis, a methodology for combining the SAR information was developed. Assuming that there are sufficient and adequate remote sensing data, the proposed fusion approach may increase the biomass maps accuracy, their spatial extension and their updated frequency. These characteristics are essential for the future derivation of accurate, global and robust forest biomass maps