Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

Développements algorithmiques pour l’amélioration des résultats de l’interférométrie RADAR en milieu urbain

Le suivi des espaces urbanisés et de leurs dynamiques spatio-temporelles représente un enjeu important pour la population urbaine, autant sur le plan environnemental, économique et social. Avec le lancement des satellites portant des radars à synthèse d’ouverture de la nouvelle génération (TerraSAR-X, COSMO-SkyMed, ALOS, RADARSAT-2,Sentinel-1, Constellation RADARSAT), il est possible d’obtenir des séries temporelles d’images avec des résolutions spatiales et temporelles fines. Ces données multitemporelles aident à mieux analyser et décrire les structures urbaines et leurs variations dans l’espace et dans le temps. L’interférométrie par satellite est effectuée en comparant les phases des images RSO prises à différents passages du satellite au-dessus du même territoire. En optant pour des positions du satellite séparées d’une longue ligne de base, l’InSAR mène à la création des modèles numériques d’altitude (MNA). Si cette ligne de base est courte et à la limite nulle, nous avons le cas de l’interférométrie différentielle (DInSAR) qui mène à l’estimation du mouvement possible du terrain entre les deux acquisitions. Pour toutes les deux applications de l’InSAR, deux opérations sont importantes qui garantissent la génération des interférogrammes de qualité. La première est le filtrage du bruit omniprésent dans les phases interférométriques et la deuxième est le déroulement des phases. Ces deux opérations deviennent particulièrement complexes en milieu urbain où au bruit des phases s’ajoutent des fréquents sauts et discontinuités des phases dus à la présence des bâtiments et d’autres structures surélevées. L’objectif de cette recherche est le développement des nouveaux algorithmes de filtrage et de déroulement de phase qui puissent mieux performer que les algorithmes considérés comme référence dans ce domaine. Le but est d’arriver à générer des produits InSAR de qualité en milieu urbain. Concernant le filtrage, nous avons établi un algorithme qui est une nouvelle formulation du filtre Gaussien anisotrope adaptatif. Quant à l’algorithme de déroulement de phase, il est fondé sur la minimisation de l’énergie par un algorithme génétique ayant recours à une modélisation contextuelle du champ de phase. Différents tests ont été effectués avec des images RSO simulées et réelles qui démontrent le potentiel de nos algorithmes qui dépasse à maints égards celui des algorithmes standard. Enfin, pour atteindre le but de notre recherche, nous avons intégré nos algorithmes dans l’environnement du logiciel SNAP et appliqué l’ensemble de la procédure pour générer un MNA avec des images RADARSAT-2 de haute résolution d’un secteur de la Ville de Montréal (Canada) ainsi que des cartes des mouvements du terrain dans la région de la Ville de Mexico (Mexique) avec des images de Sentinel-1 de résolution plutôt moyenne. La comparaison des résultats obtenus avec des données provenant des sources externes de qualité a aussi démontré le fort potentiel de nos algorithmes.The monitoring of urban areas and their spatiotemporal dynamics is an important issue for the urban population, at the environmental, economic, as well as social level. With the launch of satellites carrying next-generation synthetic aperture radars (TerraSAR-X, COSMO-SkyMed, ALOS, RADARSAT-2, Sentinel-1, Constellation RADARSAT), it is possible to obtain time series of images with fine temporal and spatial resolutions. These multitemporal data help to better analyze and describe urban structures, and their variations in space and time. Satellite interferometry is performed by comparing the phases of SAR images taken at different satellite passes over the same territory. By opt-ing for satellite positions separated by a long baseline, InSAR leads to the creation of digital elevation models (DEM). If this baseline is short and, at the limit zero, we have the case of differential interferometry (DInSAR) which leads to the estimation of the possible movement of the land between the two acquisitions. In both InSAR applica-tions, two operations are important that ensure the generation of quality interferograms. The first is the filtering of ubiquitous noise in the interferometric phases and the second is the unwrapping of the phases. These two operations become particularly complex in urban areas where the phase noise is added to the frequent jumps and discontinuities of phases due to the presence of buildings and other raised structures. The objective of this research is the development of new filtering and phase unwrap-ping algorithms that can perform better than algorithms considered as reference in this field. The goal is to generate quality InSAR products in urban areas. Regarding filtering, we have established an algorithm that is a new formulation of the adaptive anisotropic Gaussian filter. As for the phase unwrapping algorithm, it is based on the minimization of energy by a genetic algorithm using contextual modelling of the phase field. Various tests have been carried out with simulated and real SAR images that demonstrated the potential of our algorithms that in many respects exceeds that of standard algorithms. Finally, to achieve the goal of our research, we integrated our algorithms into the SNAP software environment and applied the entire procedure to generate a DEM with high-resolution RADARSAT-2 images from an area of the City of Montreal (Canada) as well as maps of land movement in the Mexico City region (Mexico) with relatively medium-resolution Sentinel-1 images. Comparison of the results with data from external quality sources also demonstrated the strong potential of our algorithms

Interferometric Synthetic Aperture Sonar Signal Processing for Autonomous Underwater Vehicles Operating Shallow Water

The goal of the research was to develop best practices for image signal processing method for InSAS systems for bathymetric height determination. Improvements over existing techniques comes from the fusion of Chirp-Scaling a phase preserving beamforming techniques to form a SAS image, an interferometric Vernier method to unwrap the phase; and confirming the direction of arrival with the MUltiple SIgnal Channel (MUSIC) estimation technique. The fusion of Chirp-Scaling, Vernier, and MUSIC lead to the stability in the bathymetric height measurement, and improvements in resolution. This method is computationally faster, and used less memory then existing techniques

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

Visualization and Localization of Interventional Devices with MRI by Susceptibility Mapping

Recently, interventional procedures can be performed with the visual assistance of MRI. However, the devices used in these procedures, such as brachytherapy seeds, biopsy needles, markers, and stents, have a large magnetic susceptibility that leads to severe signal loss and distortion in the MRI images and degrades the accuracy of the localization. Right now, there is no effective way to correctly identify, localize and visualize these interventional devices in MRI images. In this dissertation, we proposed a method to improve the accuracy of localization and visualization by generating positive contrast of the interventional devices using a regularized L1 minimization algorithm. Specifically, the spin-echo sequence with a shifted 180-degree pulse is used to acquire high SNR data. A short shift time is used to avoid severe phase wrap. A phase unwrapping method based on Markov Random Field using Highest-Confidence-First algorithm is proposed to unwrap the phase image. Then the phase images with different shifted time are used to calculate the field map. Next, L1 regularized deconvolution is performed to calculate the susceptibility map. With much higher susceptibility of the interventional devices than the background tissue, the interventional devices show positive-contrast in the susceptibility image. Computer simulations were performed to study the effect of the signal-to-noise ratio, resolution, orientation and size of the interventional devices on the accuracy of the results. Experiments were performed using gelatin and tissue phantom with brachytherapy seeds, gelatin phantoms with platinum wires, and water phantom with titanium needles. The results show that the proposed method provide positive contrast images of these interventional devices, differentiate them from other structures in the MRI images, and improves the visualization and localization of the devices

Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

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Novel Approaches in Landslide Monitoring and Data Analysis

Significant progress has been made in the last few years that has expanded the knowledge of landslide processes. It is, therefore, necessary to summarize, share and disseminate the latest knowledge and expertise. This Special Issue brings together novel research focused on landslide monitoring, modelling and data analysis