Coherent change detection and interferometric ISAR measurements in the folded compact range

A folded compact range configuration has been developed ant the Sandia National Laboratories` compact range antenna and radar-cross- section measurement facility as a means of performing indoor, environmentally-controlled, far-field simulations of synthetic aperture radar (SAR) measurements of distributed target samples (i.e. gravel, sand, etc.). The folded compact range configuration has previously been used to perform coherent-change-detection (CCD) measurements, which allow disturbances to distributed targets on the order of fractions of a wavelength to be detected. This report describes follow-on CCD measurements of other distributed target samples, and also investigates the sensitivity of the CCD measurement process to changes in the relative spatial location of the SAR sensor between observations of the target. Additionally, this report describes the theoretical and practical aspects of performing interferometric inverse-synthetic-aperture-radar (IFISAR) measurements in the folded compact range environment. IFISAR measurements provide resolution of the relative heights of targets with accuracies on the order of a wavelength. Several examples are given of digital height maps that have been generated from measurements performed at the folded compact range facility

Virtual multichannel SAR for ground moving target imaging

Slow moving ground targets are invisible within synthetic aperture radar (SAR) images since they appear defocused and their backscattered signal completely overlap the focused ground return. In order for this targets to be detected and refocused the availability of some spatial degrees of freedom is required. This allows for space/slow time processing to be applied to mitigate the ground clutter. However, multichannel SAR (M-SAR) systems are very expensive and the requirements in terms of baseline length can be very restrictive. In this study a processing scheme that exploits high PRF single channel SAR system to emulate a multichannel SAR is presented. The signal model for both target and clutter components are presented and the difference with respect to an actual M-SAR are highlighted. The effectiveness of the proposed processing is then demonstrated on simulated a measured dataset

Investigation of ground moving target indication techniques for a multi-channel synthetic aperture radar

Synthetic Aperture Radar (SAR) is an imaging technique that creates two dimensional images of the scattering objects in the illuminated ground scene. The objects in the illuminated ground scene may be truly stationary, e.g. buildings etc. or in motion relative to these stationary objects, e.g. cars on a highway. In SAR, the radar platform is moving during the imaging period, hence everything that the radar illuminates has motion relative to the radar platform. In order to specifically detect objects on the ground that are moving relative to stationary ground objects (often termed clutter), processing techniques called Ground Moving Target Indication (GMTI) techniques are required. This is especially required for targets that are moving at relative velocities lower than the stationary clutter's relative velocity to the radar platform (endo-clutter detection). This dissertation investigates five multichannel GMTI techniques being Displaced Phase Centre Antenna (DPCA), Along Track Interferometry (ATI), Iterative Adaptive Approach (IAA), Space Time Adaptive Processing (STAP) and Velocity SAR (VSAR) in literature and assesses the performance of two selected GMTI techniques (ATI and DPCA) on simulated and measured radar data to compare them and identify their strengths and weaknesses. The radar data were measured with a C-band FMCW radar in a controlled environment with known parameters and cooperating targets. The performances of the techniques were assessed in terms of moving target detection within clutter and sensitivity to inaccuracies in the physical system setup. The DPCA technique exhibited some attractive characteristics over the ATI technique. These included its robustness against false alarm in noise dominated cells - ATI exhibited large phase residuals in noise dominated cells, due to the random nature of the phase in these cells. Furthermore, DPCA seem to not suffer from false alarms due to volumetric scattering of vegetation to the extent that was observed with ATI. Lastly, DPCA exhibited more robustness against temporal misalignment errors introduced between the measurement channels, compared to ATI. These observations lead to the conclusion that DPCA would be a practically better choice to implement for the purpose of moving target detection, compared to ATI. However, a double threshold approach, which used DPCA as a pre-processing step to ATI, proved to be superior to DPCA alone in terms of moving target indication within clutter and noise. This approach was verified through implementation on the measured radar data in this study

Sentinel-1 InSAR coherence for land cover mapping: a comparison of multiple feature-based classifiers

This article investigates and demonstrates the suitability of the Sentinel-1 interferometric coherence for land cover and vegetation mapping. In addition, this study analyzes the performance of this feature along with polarization and intensity products according to different classification strategies and algorithms. Seven different classification workflows were evaluated, covering pixel- and object-based analyses, unsupervised and supervised classification, different machine-learning classifiers, and the various effects of distinct input features in the SAR domain—interferometric coherence, backscattered intensities, and polarization. All classifications followed the Corine land cover nomenclature. Three different study areas in Europe were selected during 2015 and 2016 campaigns to maximize diversity of land cover. Overall accuracies (OA), ranging from 70% to 90%, were achieved depending on the study area and methodology, considering between 9 and 15 classes. The best results were achieved in the rather flat area of Doñana wetlands National Park in Spain (OA 90%), but even the challenging alpine terrain around the city of Merano in northern Italy (OA 77%) obtained promising results. The overall potential of Sentinel-1 interferometric coherence for land cover mapping was evaluated as very good. In all cases, coherence-based results provided higher accuracies than intensity-based strategies, considering 12 days of temporal sampling of the Sentinel-1 A stack. Both coherence and intensity prove to be complementary observables, increasing the overall accuracies in a combined strategy. The accuracy is expected to increase when Sentinel-1 A/B stacks, i.e., six-day sampling, are considered.Peer ReviewedPostprint (published version

Elevation and Deformation Extraction from TomoSAR

3D SAR tomography (TomoSAR) and 4D SAR differential tomography (Diff-TomoSAR) exploit multi-baseline SAR data stacks to provide an essential innovation of SAR Interferometry for many applications, sensing complex scenes with multiple scatterers mapped into the same SAR pixel cell. However, these are still influenced by DEM uncertainty, temporal decorrelation, orbital, tropospheric and ionospheric phase distortion and height blurring. In this thesis, these techniques are explored. As part of this exploration, the systematic procedures for DEM generation, DEM quality assessment, DEM quality improvement and DEM applications are first studied. Besides, this thesis focuses on the whole cycle of systematic methods for 3D & 4D TomoSAR imaging for height and deformation retrieval, from the problem formation phase, through the development of methods to testing on real SAR data. After DEM generation introduction from spaceborne bistatic InSAR (TanDEM-X) and airborne photogrammetry (Bluesky), a new DEM co-registration method with line feature validation (river network line, ridgeline, valley line, crater boundary feature and so on) is developed and demonstrated to assist the study of a wide area DEM data quality. This DEM co-registration method aligns two DEMs irrespective of the linear distortion model, which improves the quality of DEM vertical comparison accuracy significantly and is suitable and helpful for DEM quality assessment. A systematic TomoSAR algorithm and method have been established, tested, analysed and demonstrated for various applications (urban buildings, bridges, dams) to achieve better 3D & 4D tomographic SAR imaging results. These include applying Cosmo-Skymed X band single-polarisation data over the Zipingpu dam, Dujiangyan, Sichuan, China, to map topography; and using ALOS L band data in the San Francisco Bay region to map urban building and bridge. A new ionospheric correction method based on the tile method employing IGS TEC data, a split-spectrum and an ionospheric model via least squares are developed to correct ionospheric distortion to improve the accuracy of 3D & 4D tomographic SAR imaging. Meanwhile, a pixel by pixel orbit baseline estimation method is developed to address the research gaps of baseline estimation for 3D & 4D spaceborne SAR tomography imaging. Moreover, a SAR tomography imaging algorithm and a differential tomography four-dimensional SAR imaging algorithm based on compressive sensing, SAR interferometry phase (InSAR) calibration reference to DEM with DEM error correction, a new phase error calibration and compensation algorithm, based on PS, SVD, PGA, weighted least squares and minimum entropy, are developed to obtain accurate 3D & 4D tomographic SAR imaging results. The new baseline estimation method and consequent TomoSAR processing results showed that an accurate baseline estimation is essential to build up the TomoSAR model. After baseline estimation, phase calibration experiments (via FFT and Capon method) indicate that a phase calibration step is indispensable for TomoSAR imaging, which eventually influences the inversion results. A super-resolution reconstruction CS based study demonstrates X band data with the CS method does not fit for forest reconstruction but works for reconstruction of large civil engineering structures such as dams and urban buildings. Meanwhile, the L band data with FFT, Capon and the CS method are shown to work for the reconstruction of large manmade structures (such as bridges) and urban buildings

Bayesian super-resolution with application to radar target recognition

This thesis is concerned with methods to facilitate automatic target recognition using images generated from a group of associated radar systems. Target recognition algorithms require access to a database of previously recorded or synthesized radar images for the targets of interest, or a database of features based on those images. However, the resolution of a new image acquired under non-ideal conditions may not be as good as that of the images used to generate the database. Therefore it is proposed to use super-resolution techniques to match the resolution of new images with the resolution of database images. A comprehensive review of the literature is given for super-resolution when used either on its own, or in conjunction with target recognition. A new superresolution algorithm is developed that is based on numerical Markov chain Monte Carlo Bayesian statistics. This algorithm allows uncertainty in the superresolved image to be taken into account in the target recognition process. It is shown that the Bayesian approach improves the probability of correct target classification over standard super-resolution techniques. The new super-resolution algorithm is demonstrated using a simple synthetically generated data set and is compared to other similar algorithms. A variety of effects that degrade super-resolution performance, such as defocus, are analyzed and techniques to compensate for these are presented. Performance of the super-resolution algorithm is then tested as part of a Bayesian target recognition framework using measured radar data

Estimation of biophysical parameters in boreal forests from ERS and JERS SAR interferometry

The thesis describes investigations concerning the evaluation of ERS and JERS SAR images and repeat-pass interferometric SAR images for the retrieval of biophysical parameters in boreal forests. The availability of extensive data sets of images over several test sites located in Sweden, Finland and Siberia has allowed analysis of temporal dynamics of ERS and JERS backscatter and coherence, and of ERS interferometric phase. Modelling of backscatter, coherence and InSAR phase has been performed by means of the Water Cloud Model (WCM) and the Interferometric Water Cloud Model (IWCM); sensitivity analysis and implications for the retrieval of forest biophysical parameters have been thoroughly discussed. Model inversion has been carried out for stem volume retrieval using ERS coherence, ERS backscatter and JERS backscatter, whereas for tree height estimation the ERS interferometric phase has been used. Multi-temporal combination of ERS coherence images, and to a lesser extent of JERS backscatter images, can provide stem volume estimates comparable to stand-wise ground-based measurements. Since the information content of the interferometric phase is strongly degraded by phase noise and uncorrected atmospheric artefacts, the retrieved tree height shows large errors