Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography

This book focuses on remote sensing for urban deformation monitoring. In particular, it highlights how deformation monitoring in urban areas can be carried out using Persistent Scatterer Interferometry (PSI) and Synthetic Aperture Radar (SAR) Tomography (TomoSAR). Several contributions show the capabilities of Interferometric SAR (InSAR) and PSI techniques for urban deformation monitoring. Some of them show the advantages of TomoSAR in un-mixing multiple scatterers for urban mapping and monitoring. This book is dedicated to the technical and scientific community interested in urban applications. It is useful for choosing the appropriate technique and gaining an assessment of the expected performance. The book will also be useful to researchers, as it provides information on the state-of-the-art and new trends in this fiel

Polarization Optimization for the Detection of Multiple Persistent Scatterers Using SAR Tomography

The detection of multiple interfering persistent scatterers (PSs) using Synthetic Aperture Radar (SAR) tomography is an efficient tool for generating point clouds of urban areas. In this context, detection methods based upon the polarization information of SAR data are effective at increasing the number of PSs and producing high-density point clouds. This paper presents a comparative study on the effects of the polarization design of a radar antenna on further improving the probability of detecting persistent scatterers. For this purpose, we introduce an extension of the existing scattering property-based generalized likelihood ratio test (GLRT) with realistic dependence on the transmitted/received polarizations. The test is based upon polarization basis optimization by synthesizing all possible polarimetric responses of a given scatterer from its measurements on a linear orthonormal basis. Experiments on both simulated and real data show, by means of objective metrics (probability of detection, false alarm rate, and signal-to-noise ratio), that polarization waveform optimization can provide a significant performance gain in the detection of multiple scatterers compared to the existing full-polarization-based detection method. In particular, the increased density of detected PSs at the studied test sites demonstrates the main contribution of the proposed method

Remote Monitoring of Civil Infrastructure Based on TomoSAR

Structural health monitoring and damage detection tools are extremely important topics nowadays with the civil infrastructure aging and deteriorating problems observed in urban areas. These tasks can be done by visual inspection and by using traditional in situ methods, such as leveling or using traditional mechanical and electrical sensors, but these approaches are costly, labor-intensive and cannot be performed with a high temporal frequency. In recent years, remote sensing has proved to be a very promising methodology in evaluating the health of a structure by assessing its deformation and thermal dilation. The satellite-based Synthetic Aperture Radar Tomography (TomoSAR) technique, based on the exploitation of a stack of multi-temporal SAR images, allows to remotely sense the movement and the thermal dilation of individual structures with a centimeter-to millimeter-level accuracy, thanks to new generation high-resolution satellite-borne sensors. In this paper, the effectiveness of a recently developed TomoSAR technique in assessing both possible deformations and the thermal dilation evolution of man-made structures is shown. The results obtained using X-band SAR data in two case studies, concerning two urban structures in the city of Naples (Italy), are presented

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

A Sequential MUSIC algorithm for Scatterers Detection 2 in SAR Tomography Enhanced by a Robust Covariance 3 Estimator

Synthetic aperture radar (SAR) tomography (TomoSAR) is an appealing tool for the extraction of height information of urban infrastructures. Due to the widespread applications of the MUSIC algorithm in source localization, it is a suitable solution in TomoSAR when multiple snapshots (looks) are available. While the classical MUSIC algorithm aims to estimate the whole reflectivity profile of scatterers, sequential MUSIC algorithms are suited for the detection of sparse point-like scatterers. In this class of methods, successive cancellation is performed through orthogonal complement projections on the MUSIC power spectrum. In this work, a new sequential MUSIC algorithm named recursive covariance canceled MUSIC (RCC-MUSIC), is proposed. This method brings higher accuracy in comparison with the previous sequential methods at the cost of a negligible increase in computational cost. Furthermore, to improve the performance of RCC-MUSIC, it is combined with the recent method of covariance matrix estimation called correlation subspace. Utilizing the correlation subspace method results in a denoised covariance matrix which in turn, increases the accuracy of subspace-based methods. Several numerical examples are presented to compare the performance of the proposed method with the relevant state-of-the-art methods. As a subspace method, simulation results demonstrate the efficiency of the proposed method in terms of estimation accuracy and computational load

Elevation Extraction from Spaceborne SAR Tomography Using Multi-Baseline COSMO-SkyMed SAR Data

SAR tomography (TomoSAR) extends SAR interferometry (InSAR) to image a complex 3D scene with multiple scatterers within the same SAR cell. The phase calibration method and the super-resolution reconstruction method play a crucial role in 3D TomoSAR imaging from multi-baseline SAR stacks, and they both influence the accuracy of the 3D SAR tomographic imaging results. This paper presents a systematic processing method for 3D SAR tomography imaging. Moreover, with the newly released TanDEM-X 12 m DEM, this study proposes a new phase calibration method based on SAR InSAR and DEM error estimation with the super-resolution reconstruction compressive sensing (CS) method for 3D TomoSAR imaging using COSMO-SkyMed Spaceborne SAR data. The test, fieldwork, and results validation were executed at Zipingpu Dam, Dujiangyan, Sichuan, China. After processing, the 1 m resolution TomoSAR elevation extraction results were obtained. Against the terrestrial Lidar ‘truth’ data, the elevation results were shown to have an accuracy of 0.25 ± 1.04 m and a RMSE of 1.07 m in the dam area. The results and their subsequent validation demonstrate that the X band data using the CS method are not suitable for forest structure reconstruction, but are fit for purpose for the elevation extraction of manufactured facilities including buildings in the urban area

An Interferometric SAR Satellite Mission

The paper provides a critical review of the achievements in SAR interferometry from the ERS mission as well as from the Shuttle Radar Topography Mission SRTM. It describes the development from the original idea of the Interferometric Cartwheel to the concept of a formation flight of identical and active SAR satellites. From the experience gained from ERS and SRTM interferometric data processing as well as from the analysis of the Cartwheel concept a list of mission requirements has been set up. The most demanding one is the autonomous configuration flight of a tight x-band constellation, where the satellites fly as close as up to 30 m with a dead-band of +/- 10 m. The guidance, navigation and control considerations come to the conclusion that such a mission is feasible

Performance Improvement for SAR Tomography Based on Local Plane Model

Multilook approaches have been applied in synthetic aperture radar (SAR) tomography (TomoSAR), for improving the density and regularity of persistent scatterers reconstructed from multipass SAR images in both rural and urban regions. Multilook operations assume that all scatterers in a given neighborhood are similar in height, thereby providing additional data for recovering the position and reflectivity of a single scatterer, so that a higher signal-to-noise ratio can be achieved. This is equivalent to assuming that scatterers belonging to a local neighborhood of range-azimuth cells are located on horizontal planes. The present article generalizes this approach by adopting the so-called local plane (LP) model for TomoSAR imaging in urban areas, accounting for local variations in the height of scatterers that are not negligible. Furthermore, an LP-generalized likelihood ratio test (LP-GLRT) algorithm is developed to implement the previous idea. Compared with the multilook generalized likelihood ratio test algorithm, LP-GLRT shows better performance in the case of urban structures and terrains in experiments based on both simulated data and TerraSAR-X images