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

SAR Tomography via Nonlinear Blind Scatterer Separation

Layover separation has been fundamental to many synthetic aperture radar applications, such as building reconstruction and biomass estimation. Retrieving the scattering profile along the mixed dimension (elevation) is typically solved by inversion of the SAR imaging model, a process known as SAR tomography. This paper proposes a nonlinear blind scatterer separation method to retrieve the phase centers of the layovered scatterers, avoiding the computationally expensive tomographic inversion. We demonstrate that conventional linear separation methods, e.g., principle component analysis (PCA), can only partially separate the scatterers under good conditions. These methods produce systematic phase bias in the retrieved scatterers due to the nonorthogonality of the scatterers' steering vectors, especially when the intensities of the sources are similar or the number of images is low. The proposed method artificially increases the dimensionality of the data using kernel PCA, hence mitigating the aforementioned limitations. In the processing, the proposed method sequentially deflates the covariance matrix using the estimate of the brightest scatterer from kernel PCA. Simulations demonstrate the superior performance of the proposed method over conventional PCA-based methods in various respects. Experiments using TerraSAR-X data show an improvement in height reconstruction accuracy by a factor of one to three, depending on the used number of looks.Comment: This work has been accepted by IEEE TGRS for publicatio

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

Multiresolution Detection of Persistent Scatterers: A Performance Comparison Between Multilook GLRT and CAESAR

Persistent scatterers (PS) interferometry tools are extensively used for the monitoring of slow, long-term ground deformation. High spatial resolution is typically required in urban areas to cope with the variability of the signal, whereas in rural regions, multilook shall be implemented to improve the coverage of monitored areas. Along this line, SqueeSAR and later Component extrAction and sElection SAR (CAESAR) were introduced for the monitoring of both persistent and (decorrelating) distributed scatterers (DS). Multilook generalized likelihood ratio test (MGLRT) is a detector derived in the context of tomographic SAR processing that has been investigated for a fixed multilook degree. In this work, we address MGLRT and CAESAR in the multiresolution context characterized by a spatially variable multilook degree. We compare the two schemes for the multiresolution selection of PS and DS, highlighting the pros and cons of each scheme, particularly the peculiarities of CAESAR that have important implications at the implementation stage. A performance analysis of both detectors in case of model mismatch is also addressed. Experiments carried out with data acquired by the COSMO-SkyMed constellation support both the theoretical argumentation and the results achieved by resorting to Monte Carlo simulations

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

ADVANCEMENTS IN QUANTITATIVE PERFUSION MAGNETIC RESONANCE IMAGING (MRI) OF DEMENTIA

Alzheimer's disease (AD) affects a considerable, and increasing, part of the population. Early diagnosis of AD is very important to permit effective therapy, and minimize AD's social and economic burden. The goal of our research is to evaluate the changes of cerebral perfusion (i.e., blood flow) in the early stages of AD and the effects from hypertension.We studied volunteers with Mild Cognitive Impairment (MCI) and early AD from the Pittsburgh cohort of the Cardiovascular Health Study (CHS) Cognitive Study during a four-year follow-up. Previously, studies used referral patients who typically have more advanced AD. No perfusion data concerning the early and transitional disease stages are currently available from population studies (i.e., subjects who have been monitored longitudinally in time). There are no common techniques for perfusion quantification and image analysis so that inconsistencies are observed between/within studies, modalities, and researchers. Several advancements were achieved in preparation for the cohort study. First, we improved the accuracy and speed of brain perfusion quantification. Second, we improved the accuracy of image registration to a reference brain using quantitative validation of a registration method and performance comparison with a popular registration method. Third, we improved the method of statistical analysis for evaluating the changes of perfusion between groups. Fourth, we evaluated the changes of cerebral perfusion between cognitive groups (controls, MCIs, ADs), and hypertension and normo-tensive subgroups.Individual perfusion maps were improved by measuring and incorporating individual arrival time, saturation effects, and individual inversion efficiency. A fully deformable registration technique was shown to be more accurate than standard techniques like statistical parametric mapping to detect local perfusion changes. All of the published literature for perfusion up-to-date reported decreased perfusion in AD, but we found hyperperfusion in some regions. The regional findings imply that a hemodynamic process, at the capillary level, accompanied the neurodegenerative process. Hypertensive normal cognitive controls demonstrated hypoperfusion in regions usually involved in AD pathology. However, the effect of hypertension was attenuated after the onset of the pathological cognitive process

Deep Learning for Subtle Volcanic Deformation Detection With InSAR Data in Central Volcanic Zone

Subtle volcanic deformations point to volcanic activities, and monitoring them helps predict eruptions. Today, it is possible to remotely detect volcanic deformation in mm/year scale thanks to advances in interferometric synthetic aperture radar (InSAR). This article proposes a framework based on a deep learning model to automatically discriminate subtle volcanic deformations from other deformation types in five-year-long InSAR stacks. Models are trained on a synthetic training set. To better understand and improve the models, explainable artificial intelligence (AI) analyses are performed. In initial models, Gradient-weighted Class Activation Mapping (Grad-CAM) linked new-found patterns of slope processes and salt lake deformations to false-positive detections. The models are then improved by fine-tuning (FT) with a hybrid synthetic-real data, and additional performance is extracted by low-pass spatial filtering (LSF) of the real test set. The t-distributed stochastic neighbor embedding (t-SNE) latent feature visualization confirmed the similarity and shortcomings of the FT set, highlighting the problem of elevation components in residual tropospheric noise. After fine-tuning, all the volcanic deformations are detected, including the smallest one, Lazufre, deforming 5 mm/year. The first time confirmed deformation of Cerro El Condor is observed, deforming 9.9–17.5 mm/year. Finally, sensitivity analysis uncovered the model’s minimal detectable deformation of 2 mm/year