Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery

One  of  the  most  important  methods  to  solve  traffic  congestion  is  to detect the incident state of a roadway. This paper describes the development of a method  for  road  traffic  monitoring  aimed  at  the  acquisition  and  analysis  of remote  sensing  imagery.  We  propose  a  strategy  for  road  extraction,  vehicle detection  and incident detection  from remote sensing imagery using techniques based on neural networks, Radon transform  for angle detection and traffic-flow measurements.  Traffic-bottleneck  detection  is  another  method  that  is  proposed for recognizing incidents in both offline and real-time mode. Traffic flows and incidents are extracted from aerial images of bottleneck zones. The results show that the proposed approach has a reasonable detection performance compared to other methods. The best performance of the learning system was a detection rate of 87% and a false alarm rate of less than 18% on 45 aerial images of roadways. The performance of the traffic-bottleneck detection  method had a detection rate of 87.5%

Analysis of Polarimetric Synthetic Aperture Radar and Passive Visible Light Polarimetric Imaging Data Fusion for Remote Sensing Applications

The recent launch of spaceborne (TerraSAR-X, RADARSAT-2, ALOS-PALSAR, RISAT) and airborne (SIRC, AIRSAR, UAVSAR, PISAR) polarimetric radar sensors, with capability of imaging through day and night in almost all weather conditions, has made polarimetric synthetic aperture radar (PolSAR) image interpretation and analysis an active area of research. PolSAR image classification is sensitive to object orientation and scattering properties. In recent years, significant work has been done in many areas including agriculture, forestry, oceanography, geology, terrain analysis. Visible light passive polarimetric imaging has also emerged as a powerful tool in remote sensing for enhanced information extraction. The intensity image provides information on materials in the scene while polarization measurements capture surface features, roughness, and shading, often uncorrelated with the intensity image. Advantages of visible light polarimetric imaging include high dynamic range of polarimetric signatures and being comparatively straightforward to build and calibrate. This research is about characterization and analysis of the basic scattering mechanisms for information fusion between PolSAR and passive visible light polarimetric imaging. Relationships between these two modes of imaging are established using laboratory measurements and image simulations using the Digital Image and Remote Sensing Image Generation (DIRSIG) tool. A novel low cost laboratory based S-band (2.4GHz) PolSAR instrument is developed that is capable of capturing 4 channel fully polarimetric SAR image data. Simple radar targets are formed and system calibration is performed in terms of radar cross-section. Experimental measurements are done using combination of the PolSAR instrument with visible light polarimetric imager for scenes capturing basic scattering mechanisms for phenomenology studies. The three major scattering mechanisms studied in this research include single, double and multiple bounce. Single bounce occurs from flat surfaces like lakes, rivers, bare soil, and oceans. Double bounce can be observed from two adjacent surfaces where one horizontal flat surface is near a vertical surface such as buildings and other vertical structures. Randomly oriented scatters in homogeneous media produce a multiple bounce scattering effect which occurs in forest canopies and vegetated areas. Relationships between Pauli color components from PolSAR and Degree of Linear Polarization (DOLP) from passive visible light polarimetric imaging are established using real measurements. Results show higher values of the red channel in Pauli color image (|HH-VV|) correspond to high DOLP from double bounce effect. A novel information fusion technique is applied to combine information from the two modes. In this research, it is demonstrated that the Degree of Linear Polarization (DOLP) from passive visible light polarimetric imaging can be used for separation of the classes in terms of scattering mechanisms from the PolSAR data. The separation of these three classes in terms of the scattering mechanisms has its application in the area of land cover classification and anomaly detection. The fusion of information from these particular two modes of imaging, i.e. PolSAR and passive visible light polarimetric imaging, is a largely unexplored area in remote sensing and the main challenge in this research is to identify areas and scenarios where information fusion between the two modes is advantageous for separation of the classes in terms of scattering mechanisms relative to separation achieved with only PolSAR

Détection de bateaux dans les images de radar à ouverture synthétique

Le but principal de cette thèse est de développer des algorithmes efficaces et de concevoir un système pour la détection de bateaux dans les images Radar à Ouverture Synthetique (ROS.) Dans notre cas, la détection de bateaux implique en premier lieu la détection de cibles de points dans les images ROS. Ensuite, la détection d'un bateau proprement dit dépend des propriétés physiques du bateau lui-même, tel que sa taille, sa forme, sa structure, son orientation relative a la direction de regard du radar et les conditions générales de l'état de la mer. Notre stratégie est de détecter toutes les cibles de bateaux possibles dans les images de ROS, et ensuite de chercher autour de chaque candidat des évidences telle que les sillons. Les objectifs de notre recherche sont (1) d'améliorer 1'estimation des paramètres dans Ie modèle de distribution-K et de déterminer les conditions dans lesquelles un modèle alternatif (Ie Gamma, par exemple) devrait être utilise plutôt; (2) d'explorer Ie modèle PNN (Probabilistic Neural Network) comme une alternative aux modèles paramétriques actuellement utilises; (3) de concevoir un modèle de regroupement flou (FC : Fuzzy Clustering) capable de détecter les petites et grandes cibles de bateaux dans les images a un seul canal ou les images a multi-canaux; (4) de combiner la détection de sillons avec la détection de cibles de bateaux; (5) de concevoir un modèle de détection qui peut être utilisé aussi pour la détection des cibles de bateaux en zones costières.Abstract: The main purpose of this thesis is to develop efficient algorithms and design a system for ship detection from Synthetic Aperture Radar (SAR) imagery. Ship detection usually involves through detection of point targets on a radar clutter background.The detection of a ship depends on the physical properties of the ship itself, such as size, shape, and structure; its orientation relative to the radar look-direction; and the general condition of the sea state. Our strategy is to detect all possible ship targets in SAR images, and then search around each candidate for the wake as further evidence.The objectives of our research are (1) to improve estimation of the parameters in the K-distribution model and to determine the conditions in which an alternative model (Gamma, for example) should be used instead; (2) to explore a PNN (Probabilistic Neural Networks) model as an alternative to the commonly used parameteric models; (3) to design a FC (Fuzzy Clustering) model capable of detecting both small and large ship targets from single-channel images or multi-channel images; (4) to combine wake detection with ship target detection; (5) to design a detection model that can also be used to detect ship targets in coastal areas. We have developed algorithms for each of these objectives and integrated them into a system comprising six models.The system has been tested on a number of SAR images (SEASAT, ERS and RADARSAT-1, for example) and its performance has been assessed

Coastal bathymetry from satellite high resolution monitoring

[EN] Bathymetry is traditionally obtained by echo sounding technology. However, bathymetry can be also obtained from satellite imaging, which is much more cheaper than echo-sound measurements. This is obtained by analyzing the waves near to the shoreline. In order so, wave properties such as wavelength and celerity should be measured, after which the bathymetry is estimated using linear wave theory. In this internship a new method based in the continuous wavelet transform has been implemented. In order to obtain the celerity, two images with a time lag are needed. Two data sets are used. On the one hand a video product, with 12 Pléiades images with a time lag between them of 8s. On the other hand a set of Sentinel-2 images. In the latter, a time shift between bands because of a lag in the acquisition is exploited. An application for the extraction and preparation of Sentinel-2 data in a form of a Graphical User Interface has been implemented. The site that has been studied will be the shore of Capbreton, which hosts one of the world’s deepest canyons. The images have been be pre-filtered by using FFT and Radon filters, with several methods that include windowing of fixed and variable size. Those filtering techniques have be implemented and its results compared. Best results are obtained using a variable-size windowing technique. Finally, the wavelet method has been applied to both datasets to achieve wave propagation information.Soñes Bori, J. (2019). Coastal bathymetry from satellite high resolution monitoring. Universitat Politècnica de València. http://hdl.handle.net/10251/140103TFG

Development and Characterization of a Chromotomosynthetic Hyperspectral Imaging System

A chromotomosynthetic imaging (CTI) methodology based upon mathematical reconstruction of a set of 2-D spectral projections to collect high-speed (100 Hz) 3-D hyperspectral data cube has been proposed. The CTI system can simultaneously provide usable 3-D spatial and spectral information, provide high-frame rate slitless 1-D spectra, and generate 2-D imagery equivalent to that collected with no prism in the optical system. The wavelength region where prism dispersion is highest (500 nm) is most sensitive to loss of spectral resolution in the presence of systematic error, while wavelengths 600 nm suffer mostly from a shift of the spectral peaks. The quality of the spectral resolution in the reconstructed hyperspectral imagery was degraded by as much as a factor of two in the blue spectral region with less than 1° total angular error in mount alignment in the two axes of freedom. Even with no systematic error, spatial artifacts from the reconstruction limit the ability to provide adequate spectral imagery without specialized image reconstruction techniques as targets become more spatially and spectrally uniform

Autonomous real-time infrared detection of sub-surface vessels for unmanned aircraft systems

The threat of small self-propelled semi-submersible vessels cannot be understated; payloads from drugs to weapons of mass destruction could be housed in these small, inconspicuous vessels. With a current apprehension rate of approximately 10%, a method resulting in increased interdiction of this illegal traffic is required for national security both in the ports along the coastlines of Canada, as well as the rest of North America. A smart, autonomous payload containing an infrared imaging device, designed for use in small unmanned aircraft systems for the specific mission of detecting self-propelled semi-submersibles over the vast ocean coastline will address the current security needs. Thermal imagery of the disturbed colder water layers, driven to the surface by the vessel will allow for the detection of this traffic using long wave infrared technology. Infrared signatures of ship wakes are highly variable in both persistence and temperature contrast as compared to the surrounding surface water, thus infrared imaging devices with a high resolution, a high responsivity, and a very low minimum resolvable temperature will be required to provide high quality imagery for airborne detection of the thermal wake. A theoretical understanding of the physics associated with the energy collected by the infrared sensor and the resulting infrared images is provided. Explanation of the factors affecting the resulting image with respect to the camera properties are detailed. A variety of examples of airborne thermal images are presented, with detailed explanations of the imaged scenes based on theory and sensor characteristics provided in the previous sections. Infrared images taken over the Atlantic and Pacific oceans from manned and unmanned aircraft platforms are presented. Temperature measurements taken using Vemco Minilog II temperature loggers confirmed the thermal stratification of the upper 5 meters of the water. Thermal scarring due to upwelled colder water to the surface was noted during the day time under normal conditions, with temperature differences found to be consistent with the measured temperature profile. A custom gimbal system, with corresponding ground control station for real-time, visual feedback is presented. An algorithm for the detection of submerged vessel ship wakes using a LWIR camera, specifically for a small unmanned aircraft, with limited power, space, and computing power is developed. A time sequential processing method is presented to reduce the required computing, while allowing high frame rate, real-time operation. Moreover, a windowed triple-vote method is continually applied to ensure that the detection mode is correctly set by the algorithm, while ignoring unexpected targets in the image. A simple background estimation method is presented to remove any nonuniformity in the captured images, resulting in a high detection rate with low false alarms. Finally, a complete, mission-ready payload system is prepared for small UA platforms, with an accuracy rate greater than 97% for the detection of self-propelled semi-submersible vessels

Scattering Center Extraction and Recognition Based on ESPRIT Algorithm

Inverse Synthetic Aperture Radar (ISAR) generates high quality radar images even in low visibility. And it provides important physical features for space target recognition and location. This thesis focuses on ISAR rapid imaging, scattering center information extraction, and target classification. Based on the principle of Fourier imaging, the backscattering field of radar target is obtained by physical optics (PO) algorithm, and the relation between scattering field and objective function is deduced. According to the resolution formula, the incident parameters of electromagnetic wave are set reasonably. The interpolation method is used to realize three-dimensional (3D) simulation of aircraft target, and the results are compared with direct imaging results. CLEAN algorithm extracts scattering center information effectively. But due to the limitation of resolution parameters, traditional imaging can’t meet the actual demand. Therefore, the super-resolution Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm is used to obtain spatial target location information. The signal subspace and noise subspace are orthogonal to each other. By combining spatial smoothing method with ESPRIT algorithm, the physical characteristics of geometric target scattering center are obtained accurately. In particular, the proposed method is validated on complex 3D aircraft targets and it proves that this method is applied to most scattering mechanisms. The distribution of scattering centers reflects the geometric information of the target. Therefore, the electromagnetic image to be recognized and ESPRIT image are matched by the domain matching method. And the classification results under different radii are obtained. In addition, because the neural network can extract rich image features, the improved ALEX network is used to classify and recognize target data processed by ESPRIT. It proves that ESPRIT algorithm can be used to expand the existing datasets and prepare for future identification of targets in real environments. Final a visual classification system is constructed to visually display the results

On the use of the Radon transform to estimate longshore currents from video imagery

International audienc

Hardware-Accelerated SAR Simulation with NVIDIA-RTX Technology

Synthetic Aperture Radar (SAR) is a critical sensing technology that is notably independent of the sensor-to-target distance and has numerous cross-cutting applications, e.g., target recognition, mapping, surveillance, oceanography, geology, forestry (biomass, deforestation), disaster monitoring (volcano eruptions, oil spills, flooding), and infrastructure tracking (urban growth, structure mapping). SAR uses a high-power antenna to illuminate target locations with electromagnetic radiation, e.g., 10GHz radio waves, and illuminated surface backscatter is sensed by the antenna which is then used to generate images of structures. Real SAR data is difficult and costly to produce and, for research, lacks a reliable source ground truth. This article proposes a open source SAR simulator to compute phase histories for arbitrary 3D scenes using newly available ray-tracing hardware made available commercially through the NVIDIA's RTX graphics cards series. The OptiX GPU ray tracing library for NVIDIA GPUs is used to calculate SAR phase histories at unprecedented computational speeds. The simulation results are validated against existing SAR simulation code for spotlight SAR illumination of point targets. The computational performance of this approach provides orders of magnitude speed increases over CPU simulation. An additional order of magnitude of GPU acceleration when simulations are run on RTX GPUs which include hardware specifically to accelerate OptiX ray tracing. The article describes the OptiX simulator structure, processing framework and calculations that afford execution on massively parallel GPU computation device. The shortcoming of the OptiX library's restriction to single precision float representation is discussed and modifications of sensitive calculations are proposed to reduce truncation error thereby increasing the simulation accuracy under this constraint.Comment: 17 pages, 7 figures, Algorithms for Synthetic Aperture Radar Imagery XXVII, SPIE Defense + Commercial Sensing 202