Experimental low-THz imaging radar for automotive applications

This thesis reports initial experimental results that provide the foundation for low-THz radar imagery for outdoor scenarios as expected in automotive sensing. The requirements for a low-THz single imaging radar sensor are outlined. The imaging capability of frequency-modulated continuous-wave (FMCW) radar operating at 150 GHz is discussed. A comparison of experimental images of on-road and off- road scenarios made by a 150 GHz FMCW radar and a reference 30 GHz stepped frequency radar is made, and their performance is analysed

Experimental low-THz imaging radar for automotive applications

This thesis reports initial experimental results that provide the foundation for low-THz radar imagery for outdoor scenarios as expected in automotive sensing. The requirements for a low-THz single imaging radar sensor are outlined. The imaging capability of frequency-modulated continuous-wave (FMCW) radar operating at 150 GHz is discussed. A comparison of experimental images of on-road and off- road scenarios made by a 150 GHz FMCW radar and a reference 30 GHz stepped frequency radar is made, and their performance is analysed

Remote Sensing methods for power line corridor surveys

AbstractTo secure uninterrupted distribution of electricity, effective monitoring and maintenance of power lines are needed. This literature review article aims to give a wide overview of the possibilities provided by modern remote sensing sensors in power line corridor surveys and to discuss the potential and limitations of different approaches. Monitoring of both power line components and vegetation around them is included. Remotely sensed data sources discussed in the review include synthetic aperture radar (SAR) images, optical satellite and aerial images, thermal images, airborne laser scanner (ALS) data, land-based mobile mapping data, and unmanned aerial vehicle (UAV) data. The review shows that most previous studies have concentrated on the mapping and analysis of network components. In particular, automated extraction of power line conductors has achieved much attention, and promising results have been reported. For example, accuracy levels above 90% have been presented for the extraction of conductors from ALS data or aerial images. However, in many studies datasets have been small and numerical quality analyses have been omitted. Mapping of vegetation near power lines has been a less common research topic than mapping of the components, but several studies have also been carried out in this field, especially using optical aerial and satellite images. Based on the review we conclude that in future research more attention should be given to an integrated use of various data sources to benefit from the various techniques in an optimal way. Knowledge in related fields, such as vegetation monitoring from ALS, SAR and optical image data should be better exploited to develop useful monitoring approaches. Special attention should be given to rapidly developing remote sensing techniques such as UAVs and laser scanning from airborne and land-based platforms. To demonstrate and verify the capabilities of automated monitoring approaches, large tests in various environments and practical monitoring conditions are needed. These should include careful quality analyses and comparisons between different data sources, methods and individual algorithms

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

D5.1 SHM digital twin requirements for residential, industrial buildings and bridges

This deliverable presents a report of the needs for structural control on buildings (initial imperfections, deflections at service, stability, rheology) and on bridges (vibrations, modal shapes, deflections, stresses) based on state-of-the-art image-based and sensor-based techniques. To this end, the deliverable identifies and describes strategies that encompass state-of-the-art instrumentation and control for infrastructures (SHM technologies).Objectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement EconòmicObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPreprin

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

CMOS Image Sensors in Surveillance System Applications

Recent technology advances in CMOS image sensors (CIS) enable their utilization in the most demanding of surveillance fields, especially visual surveillance and intrusion detection in intelligent surveillance systems, aerial surveillance in war zones, Earth environmental surveillance by satellites in space monitoring, agricultural monitoring using wireless sensor networks and internet of things and driver assistance in automotive fields. This paper presents an overview of CMOS image sensor-based surveillance applications over the last decade by tabulating the design characteristics related to image quality such as resolution, frame rate, dynamic range, signal-to-noise ratio, and also processing technology. Different models of CMOS image sensors used in all applications have been surveyed and tabulated for every year and application.https://doi.org/10.3390/s2102048

Road Scene Interpretation for Autonomous Navigation Fusing Stereo Vision and Digital Maps

En esta tesis se ha presentado un método de detección de carretera basado en visión estereoscópica. El aprendizaje automático se utiliza para resolver problemas de visión artificial de muy diferente ámbito, en concreto, la técnica utilizada en este caso es la llamada boosting, la cual utiliza árboles de decisión para clasificar cada píxel de la imagen como zona que pertenece carretera o no. El vector de características utilizado incluye información proporcionada por mapas digitales, visión estéreo y cámaras en color y en escala de grises. La imagen en escala de grises es utilizada para detectar marcas viales, Local Binary Patterns (LBP) y Histogramas de Orientación de Gradiente (HOG). Las cámaras en color son utilizadas para el cálculo de una imagen que es invariante a la iluminación y también para detectar las sombras presentes en la imagen. Además, se ha desarrollado un método basado en el espacio de color HSV para detectar las zonas de vegetación presentes en la escena. Las cámaras estéreo tienen un papel importante porque son las encargadas de proporcionar información 3D al sistema. Algunas de las características que usan dicha información son los vectores normales y los valores de curvatura. Se ha desarrollado un nuevo método para la detección de bordillos. Este novedoso detector de bordillos se basa en el análisis de la curvatura porque describe la variación de la forma de la carretera incluso en presencia de pequeños bordillos. La función es capaz de detectar bordillos de 3 cm de altura incluso hasta 20 metros de distancia, siempre y cuando los píxeles que pertenecen al bordillo estén conectados entre si en la imagen de curvatura. Otros obstáculos como vehículos, muros o arboles son también detectados utilizando visión estereoscópica. Una nueva forma para convertir características que describen limites de carretera en características que describen zonas de carretera se ha descrito en esta tesis. Utiliza marcas viales, bordillos, obstáculos y zonas de vegetación como entradas y tras incluir información adicional del mapa se genera un modelo de carretera. La originalidad de este sistema es el punto desde donde se detecta es espacio libre. %Otros métodos crean lineas desde el punto medio del limite inferior de la imagen hasta que la linea llega a un obstáculo, pero nuestra propuesta utiliza otro punto de vista porque sus lineas empiezan desde el punto de fuga y los valores de las características de van acumulando a lo largo de dicha linea. Otra característica muy importante es la obtenida a partir de los mapas digitales. El objetivo es conseguir un imagen a priori de la forma de la carretera basado en la posición actual del vehículo y la información de las calles proporcionada por el mapa. La incertidumbre sobre los errores de posicionamiento son tenidos en cuenta durante el proceso y la anchura de la carretera es correctamente detectada usando el modelo radial propuesto. Se han realizado múltiples pruebas con diferentes clasificadores y parámetros basados en arboles de decisión para posteriormente elegir el clasificador que mejor funciona en la detección de carretera. El resultado de la clasificación es utilizado en un CRF para filtrar la respuesta y obtener un resultado mas suave. La métrica utilizada para evaluar los clasificadores es el F-score. El sistema es evaluado en el plano imagen, el cual es el método mas común en la literatura. Sin embargo, en un escenario de conducción autónoma, el control se realiza normalmente en una imagen a vista de pájaro de la escena. Se ha adoptado el mismo método de evaluación que se utiliza en la comparador internacional de algoritmos KITTI para poder comparar nuestros resultados con otros algoritmos

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