A fast and accurate basis pursuit denoising algorithm with application to super-resolving tomographic SAR

$L_1$ regularization is used for finding sparse solutions to an underdetermined linear system. As sparse signals are widely expected in remote sensing, this type of regularization scheme and its extensions have been widely employed in many remote sensing problems, such as image fusion, target detection, image super-resolution, and others and have led to promising results. However, solving such sparse reconstruction problems is computationally expensive and has limitations in its practical use. In this paper, we proposed a novel efficient algorithm for solving the complex-valued $L_1$ regularized least squares problem. Taking the high-dimensional tomographic synthetic aperture radar (TomoSAR) as a practical example, we carried out extensive experiments, both with simulation data and real data, to demonstrate that the proposed approach can retain the accuracy of second order methods while dramatically speeding up the processing by one or two orders. Although we have chosen TomoSAR as the example, the proposed method can be generally applied to any spectral estimation problems.Comment: 11 pages, IEEE Transactions on Geoscience and Remote Sensin

Very High Resolution Tomographic SAR Inversion for Urban Infrastructure Monitoring — A Sparse and Nonlinear Tour

The topic of this thesis is very high resolution (VHR) tomographic SAR inversion for urban infrastructure monitoring. To this end, SAR tomography and differential SAR tomography are demonstrated using TerraSAR-X spotlight data for providing 3-D and 4-D (spatial-temporal) maps of an entire high rise city area including layover separation and estimation of deformation of the buildings. A compressive sensing based estimator (SL1MMER) tailored to VHR SAR data is developed for tomographic SAR inversion by exploiting the sparsity of the signal. A systematic performance assessment of the algorithm is performed regarding elevation estimation accuracy, super-resolution and robustness. A generalized time warp method is proposed which enables differential SAR tomography to estimate multi-component nonlinear motion. All developed methods are validated with both simulated and extensive processing of large volumes of real data from TerraSAR-X

HyperLISTA-ABT: An Ultra-light Unfolded Network for Accurate Multi-component Differential Tomographic SAR Inversion

Deep neural networks based on unrolled iterative algorithms have achieved remarkable success in sparse reconstruction applications, such as synthetic aperture radar (SAR) tomographic inversion (TomoSAR). However, the currently available deep learning-based TomoSAR algorithms are limited to three-dimensional (3D) reconstruction. The extension of deep learning-based algorithms to four-dimensional (4D) imaging, i.e., differential TomoSAR (D-TomoSAR) applications, is impeded mainly due to the high-dimensional weight matrices required by the network designed for D-TomoSAR inversion, which typically contain millions of freely trainable parameters. Learning such huge number of weights requires an enormous number of training samples, resulting in a large memory burden and excessive time consumption. To tackle this issue, we propose an efficient and accurate algorithm called HyperLISTA-ABT. The weights in HyperLISTA-ABT are determined in an analytical way according to a minimum coherence criterion, trimming the model down to an ultra-light one with only three hyperparameters. Additionally, HyperLISTA-ABT improves the global thresholding by utilizing an adaptive blockwise thresholding scheme, which applies block-coordinate techniques and conducts thresholding in local blocks, so that weak expressions and local features can be retained in the shrinkage step layer by layer. Simulations were performed and demonstrated the effectiveness of our approach, showing that HyperLISTA-ABT achieves superior computational efficiency and with no significant performance degradation compared to state-of-the-art methods. Real data experiments showed that a high-quality 4D point cloud could be reconstructed over a large area by the proposed HyperLISTA-ABT with affordable computational resources and in a fast time

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

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

Interferometric Synthetic Aperture RADAR and Radargrammetry towards the Categorization of Building Changes

The purpose of this work is the investigation of SAR techniques relying on multi image acquisition for fully automatic and rapid change detection analysis at building level. In particular, the benefits and limitations of a complementary use of two specific SAR techniques, InSAR and radargrammetry, in an emergency context are examined in term of quickness, globality and accuracy. The analysis is performed using spaceborne SAR data

Study of the speckle noise effects over the eigen decomposition of polarimetric SAR data: a review

This paper is focused on considering the effects of speckle noise on the eigen decomposition of the co- herency matrix. Based on a perturbation analysis of the matrix, it is possible to obtain an analytical expression for the mean value of the eigenvalues and the eigenvectors, as well as for the Entropy, the Anisotroopy and the dif- ferent a angles. The analytical expressions are compared against simulated polarimetric SAR data, demonstrating the correctness of the different expressions.Peer ReviewedPostprint (published version

Vulnerability analysis of buildings in areas affected by slow-moving landslides and subsidence phenomena

2015 - 2016Slow-moving landslides and subsidence phenomena yearly induce huge damages both direct (on structures and/or infrastructures with them interacting) and indirect (corresponding to the associated economic losses). For this reason, studies aimed at analyzing and predicting the aforementioned damages are of great interest for Scientific Community and Authorities in charge of identifying the most suitable strategies for the land-use planning and management of urban areas affected by slowmoving landslides and subsidence phenomena. However, carrying out the activities related to the pursuit of those goals is not straightforward since it usually requires high costs due to the great amount of data to be collected for setting up reliable forecasting models as well as the development of proper procedures that take into account i) the identification and quantification of the exposed elements; ii) the definition and estimation of an intensity parameter; iii) the prediction of the damage severity level (generally associated with the attainment of a certain limit state). In this PhD Thesis some original procedures are proposed. In particular, on the basis of empirical and numerical methods, fragility and vulnerability curves are generated in order to predict the damage to buildings in subsidence- and slow-moving landslide-affected areas. The proposed empirical procedures, based on the joint use of DInSAR data (provided from the processing of images acquired by Synthetic Aperture Radar via Differential Interferometric techniques) and information on damages suffered by buildings (recorded and classified during in situ surveys), were tested on case studies in The Netherlands, affected by subsidence phenomena, and in Calabria Region (southern Italy) for slow-moving landslide-affected areas. The procedure based on the adoption of a numerical method was applied on a structural model representative of a single building. With reference to subsidence phenomena, the analyses were carried out for a densely urbanized municipality following a multi-scale approach. In particular, at medium scale, the subsiding areas that are most prone to ground surface settlements along with their spatial distribution and rates, were preliminarily detected. The above ground surface settlements (here considered as subsidence intensity parameter) combined with the results of an extensive damage survey on masonry buildings, allowed first retrieving, at large-scale (on building aggregates) and at detailed scale (on single buildings), the relationships between cause (settlements/differential settlements) and effect (damage severity level); then, empirical fragility curves were generated for structurally independent single buildings. These latter were validated via their comparison with fragility curves generated, with reference to two others densely urbanized municipalities, for buildings with similar structural typology (masonry) and foundation type (shallow or deep). Finally, fragility and vulnerability curves for masonry buildings were generated by using the entire database of damages. As for slow-moving landslides, the analyses were carried out at large scale. In particular, the joint use of DInSAR and damage surveys data allowed analyzing the consequences induced on the buildings (either of masonry or reinforced concrete) with shallow foundations by retrieving the causeeffect relationships and generating empirical fragility and vulnerability curves. Finally, the numerical analyses carried out on a structural model representative of a single masonry building, allowed to go in-depth in the different aspects contributing to the onset and development of building damages as well as to quantify the uncertainties inherent to the addressed issue. The obtained results highlight the huge potential of the fragility and vulnerability curves generated according to the proposed procedures that, once further calibrated/validated and jointly used with a continuous monitoring of the intensity parameter via conventional (e.g., inclinometers, GPS, topographic leveling) and/or innovative (e.g., SAR images processed via DInSAR techniques) systems, can be valuably used as tools for the analysis and prediction of the damage to buildings for land-use planning and urban management purposes in subsidence- and slow-moving landslide-affected areas. [edited by author]Le frane a cinematica lenta e i fenomeni di subsidenza causano annualmente ingenti danni sia diretti (su strutture e/o infrastrutture con essi interagenti) che indiretti (quali si configurano le associate perdite di natura economica). Per tale ragione, gli studi volti ad analizzare e a prevedere i predetti danni sono di indubbio interesse per le Comunità e gli Enti impegnati nella individuazione delle più idonee strategie di pianificazione e di gestione delle aree urbanizzate affette dai suddetti fenomeni. Tuttavia, lo svolgimento delle attività connesse al perseguimento dei predetti obiettivi è tutt’altro che agevole in quanto richiede costi elevati, dovuti alla grande quantità di dati da acquisire per la generazione di modelli previsionali affidabili, nonché lo sviluppo di procedure che contemplino i) l’identificazione e la quantificazione degli elementi esposti, ii) la definizione e la stima di un parametro di intensità e iii) la previsione del livello di severità del danno (generalmente associato al raggiungimento di uno stato limite). La presente Tesi di Dottorato propone alcune procedure originali che, sulla base di metodi empirici e numerici, conducono alla generazione di curve di fragilità e vulnerabilità quali strumenti di previsione del danno a edifici in aree affette da frane a cinematica lenta e fenomeni di subsidenza. Le procedure empiriche proposte, basate sull’integrazione congiunta di dati DInSAR (ovvero derivanti dalla elaborazione di immagini acquisite da radar ad apertura sintetica montati su piattaforme satellitari mediante tecniche interferometriche differenziali) e sul danno subito da edifici (a sua volta classificato sulla base degli esiti di rilievi in sito dei quadri fessurativi esibiti dalle facciate), sono state testate con riferimento a casi di studio dei Paesi Bassi, affetti da fenomeni di subsidenza, e della Regione Calabria (Italia meridionale), interessati da frane a cinematica lenta. La procedura basata sull’impiego di metodi numerici è stata, invece, applicata su un modello strutturale rappresentativo di un edificio singolo. Con riferimento ai fenomeni di subsidenza, le attività svolte con un approccio multi-scalare hanno consentito preliminarmente di rilevare (a media scala) le aree che risultano essere maggiormente predisposte a cedimenti dovuti a fenomeni di subsidenza. La conoscenza della distribuzione spaziale e della entità di tali cedimenti è stata, poi, combinata con i risultati di un esteso rilievo del danno agli edifici in muratura di un’area comunale in modo da i) risalire – sia a grande scala (su aggregati di edifici) che a scala di dettaglio (singoli edifici) – alle relazioni funzionali che si stabiliscono tra causa (cedimenti assoluti/differenziali) ed effetti (livello di severità del danno) e ii) generare per singoli edifici strutturalmente indipendenti curve di fragilità su base empirica. Le curve di fragilità così calibrate sono state, poi, validate operandone un confronto con curve di fragilità generate, con la medesima procedura, per altre due aree comunali caratterizzate dalla presenza di edifici con la stessa tipologia strutturale e fondale (superficiale o profonda). Si è, infine, provveduto alla generazione di curve di fragilità e di vulnerabilità di edifici in muratura utilizzando l’intero campione di dati a disposizione. Per quanto riguarda le frane a cinematica lenta, le analisi sono state svolte esclusivamente a grande scala, dove l’uso congiunto dei dati DInSAR e del rilievo del danno a edifici in cemento armato e in muratura con fondazioni superficiali ha consentito, ancora una volta, di risalire alle relazioni causa-effetto e di generare curve di fragilità e di vulnerabilità su base empirica. Infine, l’analisi numerica effettuata su un modello strutturale rappresentativo di un singolo edificio in muratura con fondazioni superficiali ha consentito di approfondire il ruolo esercitato da alcuni fattori nella generazione e nello sviluppo del danno nonché di quantificare le incertezze che intervengono nel problema esaminato. I risultati ottenuti evidenziano l’enorme potenzialità delle curve di fragilità e vulnerabilità ottenute che, laddove ulteriormente calibrate e validate, possono essere impiegate congiuntamente con tecniche di monitoraggio in continuo dei parametri d’intensità – sia di tipo convenzionale (quali, ad esempio, inclinometri, GPS, livellazione topografica) che innovative (come quelle derivanti dall’elaborazione di immagini satellitari mediante tecniche DInSAR) – per la messa a punto di modelli previsionali utili alla pianificazione territoriale e alla gestione di aree urbane affette da frane a cinematica lenta e fenomeni di subsidenza. [a cura dell'autore]XV n.s

Opportunistic radar imaging using a multichannel receiver

Bistatic Synthetic Aperture Radars have a physically separated transmitter and receiver where one or both are moving. Besides the advantages of reduced procurement and maintenance costs, the receiving system can sense passively while remaining covert which offers obvious tactical advantages. In this work, spaceborne monostatic SARs are used as emitters of opportunity with a stationary ground-based receiver. The imaging mode of SAR systems over land is usually a wide-swath mode such as ScanSAR or TOPSAR in which the antenna scans the area of interest in range to image a larger swath at the expense of degraded cross-range resolution compared to the conventional stripmap mode. In the bistatic geometry considered here, the signals from the sidelobes of the scanning beams illuminating the adjacent sub-swath are exploited to produce images with high cross-range resolution from data obtained from a SAR system operating in wide-swath mode. To achieve this, the SAR inverse problem is rigorously formulated and solved using a Maximum A Posteriori estimation method providing enhanced cross-range resolution compared to that obtained by classical burst-mode SAR processing. This dramatically increases the number of useful images that can be produced using emitters of opportunity. Signals from any radar satellite in the receiving band of the receiver can be used, thus further decreasing the revisit time of the area of interest. As a comparison, a compressive sensing-based method is critically analysed and proves more sensitive to off-grid targets and only suited to sparse scene. The novel SAR imaging method is demonstrated using simulated data and real measurements from C-band satellites such as RADARSAT-2 and ESA’s satellites ERS-2, ENVISAT and Sentinel-1A. In addition, this thesis analyses the main technological issues in bistatic SAR such as the azimuth-variant characteristic of bistatic data and the effect of imperfect synchronisation between the non-cooperative transmitter and the receiver