Browse Topic: Remote sensing

Items (193)
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Joint Calibration of Dual LiDARs and Camera Using a Circular Chessboard2020-01-00984/14/2020
Environmental perception is a crucial subsystem in autonomous vehicles. In order to build safe and efficient traffic transportation, several researches have been proposed to build accurate, robust and real-time perception systems. Camera and LiDAR are widely equipped on autonomous self-driving cars and developed with many algorithms in recent years. The fusion system of camera and LiDAR provides state-of the-art methods for environmental perception due to the defects of single vehicular sensor. Extrinsic parameter calibration is able to align the coordinate systems of sensors and has been drawing enormous attention. However, differ from spatial alignment of two sensors’ data, joint calibration of multi-sensors (more than two sensors) should balance the degree of alignment between each two sensors. In this paper, we assemble a test platform which is made up of dual LiDARs and one monocular camera and use the same sensing hardware architecture as intelligent sweeper designed by our laboratory. Meanwhile, we propose the related joint calibration method using a circular chessboard. The center of circular chessboard is respectively detected in camera image to get pixel coordinates and in point cloud of LiDAR to get 3D coordinates. The calibration problem is then converted into a 3D-2D PnP matching problem and the center of the chessboard is set as corresponding points to construct the geometric constraints to get initial calibration values. Further, a proper global loss function is elaborately designed for Levenberg-Marquardt nonlinear optimization to obtain the final calibration parameters, and then the extrinsic parameters between any two sensors are estimated simultaneously. Experimental results show that the proposed method is suitable for the joint calibration of fusion system composed of LiDARs and camera, and the calibration results have high accuracy and stability.
Deng, ZhenwenXiong, LuYin, DongShan, Fengwu
Green's Function Extraction from Atmospheric Acoustic Propagation19AERP10_0810/1/2019
Understanding what affects acoustic waves propagating in the atmosphere is important for a variety of military applications including the development of new remote sensing techniques. Army Research Laboratory, Adelphi, Maryland Acoustic waves propagating in the atmosphere may undergo many effects including refraction by temperature and wind velocity gradients, scattering by atmospheric turbulence, absorption by the atmosphere (fluid), diffraction by terrain features, and absorption and reflection by a porous ground. As a result, there may be insonification in acoustic shadow zones, amplitude and phase fluctuations of the propagating sound signals, loss of signal coherence, changes in the interference maxima and minima of the direct ground reflected waves, and multipath effects. Understanding these effects is important for a variety of military applications, such as acoustic source localization and classification, noise propagation in the atmosphere, and the development of new remote sensing techniques of the atmosphere. By extracting the medium impulse response, or Green's function, one may obtain information about the medium channel in order to overcome the medium effects or deduce information about the medium. For example, in acoustic communications, information is sent through a medium from a host station to client stations. The transmitted information is subjected to a variety of signal distortions and noise caused by the medium. Using time-reversal processing, it is possible to extract the channel medium impulse response from the transmission of a known pilot signal through the channel medium. This Green's function was then used to modify the subsequent signals to overcome distortion in the channel.
How Dual Polarization Technique May Improve Weather Radar on Commercial Aircraft2019-01-19826/10/2019
The airborne weather radar on a commercial aircraft is essential to ensure flight safety. It is able to detect severe weather, probable areas where presence of hail may be suspected, and thanks to its Doppler capability, the wind shears that may be dangerous when taking-off or landing. However, because it operates at X-band, the picture that it offers to the pilot may be seriously biased in situation of severe weather, in reason of the attenuation of the radar wave. The adoption of the dual pol technique in this weather radar would be most beneficial for the quality of the information delivered to the pilot for the following reasons: 1 Dual pol technique allows to operate a classification of the precipitation: distinguishing rain, melting layer, snow, hail, small ice particles. 2 Dual pol technique allows correcting the return signal for attenuation in rain. The paper aims reporting recent advances in the exploitation of dual pol radar data, based on the concept of normalisation of the particle size distribution (PSD) and on ZPHI® algorithm for precipitation retrieval. Their combination helps retrieving parameter N0* able to represent alone the variability of the PSD, for rain or ice particles whatever. The main interest of the combination between dual pol radar and ZPHI® software for inflight application is: To be able to measure ice concentration in altitude by implementing a new version of ZPHI® describing the along beam N0* evolution, in order to interpret the radar reflectivity in terms of ice particle concentration; To be able to calibrate the radar using self-consistency of polarimetric variables.
Testud, Jacques VictorMoreau, EmmanuelLe Bouar, Erwan
On the Safety of Autonomous Driving: A Dynamic Deep Object Detection Approach2019-01-10444/2/2019
To improve the safety of automated driving, the paramount target of this intelligent system is to detect and segment the obstacle such as car and pedestrian, precisely. Object detection in self-driving vehicle has chiefly accomplished by making decision and detecting objects through each frame of video. However, there are diverse group of methods in both machine learning and machine vision to improve the performance of system. It is significant to factor in the function of the time in the detection phase. In other word, considering the inputs of system, which have been emitted from eclectic type of sensors such as camera, radar, and LIDAR, as time-varying signals, can be helpful to engross ‘time’ as a fundamental feature in modeling for forecasting the object, while car is moving on the way. In this paper, we focus on eliciting a model through the time to increase the accuracy of object detection in self-driving vehicles. In fact, we designed a deep recurrent neural network, which is fed by the output of a deep convolutional neural network. Eventually, the proposed system could make a decision about each front vehicle not only by utilizing the data during a predefined span of the video, but also enhance the performance of system to determine the object as whether a car or not. Results shows that the performance of system compared to convolutional neural network based vehicle detector, is more precise in the case of discerning cars.
Fekri, PedramZadeh, MehrdadDargahi, Javad
Metering structures of remote sensing instruments often have large openings or access holes. Shear panels that are X-shaped, such as those proposed for the Neutron Star Interior Composition Explorer (NICER), generally consist of C-channels and L-brackets to minimize structural distortion. This type of metering structure has large openings on the sides. Structural panels that have large access holes, such as those studied for the Landsat Operational Land Imager (OLI), generally consist of aluminum honeycomb panels with composite facesheets. Both types of metering structure require multilayer insulation (MLI) blankets to shield the internal components such as optics from sunlight and Earth albedo, and to minimize heat loss to 3K space by radiation. The issues of conventional MLI blankets for these metering structures include MLI sagging, stray light, and risk of micrometeoroid damage to optics.
An Overview of Data Transmission Used in UAVs for Remote Sensing Surveillance and Environmental Management Systems2015-36-05439/22/2015
The increasing development of Unmanned Aerial Vehicle (UAV) technologies has allowed greater use of UAVs as remote sensing platforms to enhance satellite and manned aerial vehicle remote sensing surveillance and environmental management systems. Particularly, the Brazilian National Institute for Space Research - INPE has an Environmental Data Collection System (SCD) since 1993. Recently, the MCTI (Ministry of Science, Technology and Innovation) opened the National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN). Both may need additional resources for their expansions in the near future as offered by UAV technologies. These needs illustrate the potential of UAV technologies as complement to existing or future systems. This paper presents an overview of data transmission used in UAVs for remote sensing surveillance and environmental management systems. This includes post-disaster assessment, environmental management and monitoring of infrastructure development with emphasis on imagery data communication system between the users and the aerial vehicle - link via satellite, available mobile communication networks and direct line of sight. Modulation types and allocated frequency bands for these applications are presented. These allows an image data signal captured by the surveillance camera be either displayed on the terminals in real time, or stored on ground for future off-line analysis. So, this overview aims to contribute to the expansion of current and future remote sensing surveillance and environmental management systems in Brazil using UAV technologies.
da Silva Araujo, Rodolfo AntonioRodrigues, José Antoniode Oliveira e Souza, Marcelo Lopes
VERVE is a 3D visualization system that provides situational awareness, science analysis tools, and data understanding capabilities for robotics researchers and exploration science operations. VERVE includes telemetry views that show remote system status, and can be extended to support various types of robots. VERVE is highly modular, extensible, and includes a 3D scenegraph database, interactive 3D viewer, and associated graphical user interfaces (GUIs) to OSGI (Java standards organization) plug-in based applications.
Initial Results from Radiometer and Polarimetric Radar-based Icing Algorithms Compared to In-situ Data2015-01-21536/15/2015
In early 2015, a field campaign was conducted at the NASA Glenn Research Center in Cleveland, Ohio, USA. The purpose of the campaign is to test several prototype algorithms meant to detect the location and severity of in-flight icing (or icing aloft, as opposed to ground icing) within the terminal airspace. Terminal airspace for this project is currently defined as within 25 kilometers horizontal distance of the terminal, which in this instance is Hopkins International Airport in Cleveland. Two new and improved algorithms that utilize ground-based remote sensing instrumentation have been developed and were operated during the field campaign. The first is the ‘NASA Icing Remote Sensing System’, or NIRSS. The second algorithm is the ‘Radar Icing Algorithm’, or RadIA. In addition to these algorithms, which were derived from ground-based remote sensors, in-situ icing measurements of the profiles of supercooled liquid water (SLW) collected with vibrating wire sondes attached to weather balloons produced a comprehensive database for comparison. Key fields from the SLW-sondes include air temperature, humidity and liquid water content, cataloged by time and 3-D location. This work gives an overview of the NIRSS and RadIA products and results are compared to in-situ SLW-sonde data from one icing case study. The location and quantity of supercooled liquid as measured by the insitu probes provide a measure of the utility of these prototype hazard-sensing algorithms.
Serke, DavidKing, MichaelReehorst, Andrew
Aircraft In Situ Validation of Hydrometeors and Icing Conditions Inferred by Ground-based NEXRAD Polarimetric Radar2015-01-21526/15/2015
MIT Lincoln Laboratory is tasked by the U.S. Federal Aviation Administration to investigate the use of the NEXRAD polarimetric radars* for the remote sensing of icing conditions hazardous to aircraft. A critical aspect of the investigation concerns validation that has relied upon commercial airline icing pilot reports and a dedicated campaign of in situ flights in winter storms. During the month of February in 2012 and 2013, the Convair-580 aircraft operated by the National Research Council of Canada was used for in situ validation of snowstorm characteristics under simultaneous observation by NEXRAD radars in Cleveland, Ohio and Buffalo, New York. The most anisotropic and easily distinguished winter targets to dual pol radar are ice crystals. Accordingly, laboratory diffusion chamber measurements in a tightly-controlled parameter space of temperature and humidity provide the linkage between shape and the expectation for the presence/absence of water saturation conditions necessary for icing hazard in situ. In agreement with the laboratory measurements pertaining to dendritic and hexagonal flat plate crystals, the aircraft measurements have verified the presence of supercooled water in mainly low concentrations coincident with regions showing layered anomalies of positive differential reflectivity (ZDR) by ground-based radar, otherwise known as +ZDR ‘bright bands’. Extreme values of ZDR (up to +8 dB) have also been found to be coincident with hexagonal flat plate crystals and intermittent supercooled water, also consistent with laboratory measurements. The icing conditions found with the anisotropic description are considered non-classical (condensation/collision-coalescence) and require the ascent of air and availability of ice nuclei. A modest ascent rate (<1 m/s) is needed for preservation of the anisotropic ice crystal shapes, making them identifiable to dual-pol radar. In the other limit of strong ascent (several m/s and greater), a vigorous riming process is present leading to graupel and hail, and with attendant radar reflectivity of 30 dBZ and greater. These rimed hydrometeors are also readily verified by dual pol hydrometeor classification and in situ aircraft measurements. For the intermediate level of ascent speed, snow can become rimed, diluting its anisotropy, and presents a challenge to unambiguous detection of an icing condition by dual pol radar. This challenge is under current study.
Williams, EarleDonovan, Michael F.Smalley, David J.Hallowell, Robert G.Griffin, Elaine P.Hood, Kenta T.Bennett, Betty J.Wolde, MengistuKorolev, Alexei V.
In satellite images, the true color of a body of water, or anything else they depict, is not precise. Radiometric calibration, which improves the color accuracy of an image and enables it to be used to solve remote sensing problems, has always been a costly endeavor. A cooperative effort between Stennis Space Center and Innovative Imaging and Research Corporation (I2R), a small business located on the center’s campus in southern Mississippi, is changing that.
The Neo-Geography Toolkit (NGT) is a collection of open-source software tools for the automated processing of geospatial data, including images and maps. It can process raw raster data from remote sensing instruments and transform it into useful cartographic products such as visible image base maps, topographic models, etc. It can also perform data processing on extremely large geospatial data sets (up to several tens of terabytes) via parallel processing pipelines. Finally, it can transform raw metadata, vector data, and geo-tagged datasets into standard Neo-Geography data formats such as KML.
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