GeoVisipedia (Geospatial Visual Wikipedia) is a new and novel approach to sharing knowledge about complex geospatial entities such as facilities. Facilities are composed of interconnected objects such as buildings, chemical processing units, electrical generation equipment and similar structures. Satellite imagery of a facility reveals a great deal about the organization and visual appearance of objects in a facility, but very little about the identity or function of the object. For example, given a satellite imagery of an oil refinery, an expert in refining readily identifies distillation units and can explain how they work. A non-expert would have a very difficult time identifying these objects let alone explaining how they function.
To make highly complex information accessible to non-experts, GeoVisipedia associates a wiki page with objects in satellite imagery. A user selects an object in the image and a wiki page appears that provides the user with detailed information about the object. Experts can author information into the wiki and this information is shared with other users. Additionally, GeoVisipedia automatically transfers all wiki pages from one image of a facility to other imagery of the facility. Consequentially, knowledge about objects in the facility integrates over time as new imagery becomes available and as new wiki pages are created and additional information is added to existing wiki pages. In this respect, satellite imagery becomes a portal to expert knowledge and insight about objects in a facility.
The Advance Radiographic Capability (ARC) at the National Ignition Facility (NIF) is a laser system that employs up to
four petawatt (PW) lasers to produce a sequence of short-pulse kilo-Joule laser pulses with controllable delays that
generate X-rays to provide backlighting for high-density internal confinement fusion (ICF) capsule targets. Multi-frame,
hard-X-ray radiography of imploding NIF capsules is a capability which is critical to the success of NIF's missions. ARC
is designed to employ up to eight backlighters with tens-of-picosecond temporal resolution, to record the dynamics and
produce an X-ray "motion picture" of the compression and ignition of cryogenic deuterium-tritium targets. ARC will
generate tens-of-picosecond temporal resolution during the critical phases of ICF shots. Additionally, ARC supports a
variety of other high energy density experiments including fast ignition studies on NIF. The automated alignment image
analysis algorithms use digital camera sensor images to direct ARC beams onto the tens-of-microns scale metal wires.
This paper describes the ARC automatic alignment sequence throughout the laser chain from pulse initiation to target
with an emphasis on the image processing algorithms that generate the crucial alignment positions for ARC. The image
processing descriptions and flow diagrams detail the alignment control loops throughout the ARC laser chain beginning
in the ARC high-contrast front end (HCAFE), on into the ARC main laser area, and ending in the ARC target area.
The Advanced Radiographic Capability (ARC) at the National Ignition Facility (NIF) is a petawatt-class, short-pulse laser system designed to provide x-ray backlighting of NIF targets. ARC uses four NIF beamlines to produce eight beamlets to create a sequence of eight images of an imploding fuel capsule using backlighting targets and diagnostic instrumentation. ARC employs a front end that produces two pulses, chirps the pulses out to 2 ns, and then injects the pulses into the two halves of each of four NIF beamlines. These pulses are amplified by NIF pre- and main amplifiers and transported to compressor vessels located in the NIF target area. The pulses are then compressed and pointed into the NIF target chamber where they impinge upon an array of backlighters. The interaction of the ARC laser pulses and the backlighting material produces bursts of high-energy x-rays that illuminate an imploding fuel capsule. The transmitted x-rays are imaged by diagnostic instrumentation to produce a sequence of radiograph images. A key component of the success of ARC is the automatic alignment system that accomplishes the precise alignment of the beamlets to avoid damaging equipment and to ensure that the beamlets are directed onto the tens-of-microns scale backlighters. In this paper, we describe the ARC automatic alignment system, with emphasis on control loops used to align the beampaths. We also provide a detailed discussion of the alignment image processing, because it plays a critical role in providing beam centering and pointing information for the control loops.
Four of the 192 beams of the National Ignition Facility (NIF) are currently being diverted into the Advanced Radiographic Capability (ARC) system to generate a sequence of short (1-50 picoseconds) 1053 nm laser pulses. When focused onto high Z wires in vacuum, these pulses create high energy x-ray pulses capable of penetrating the dense, imploding fusion fuel plasma during ignition scale experiments. The transmitted x-rays imaged with x-ray diagnostics can create movie radiographs that are expected to provide unprecedented insight into the implosion dynamics. The resulting images will serve as a diagnostic for tuning the experimental parameters towards successful fusion reactions. Beam delays introduced into the ARC pulses via independent, free-space optical trombones create the desired x-ray image sequence, or movie. However, these beam delays cause optical distortion of various alignment fiducials viewed by alignment sensors in the NIF and ARC beamlines. This work describes how the position of circular alignment fiducials is estimated in the presence of distortion.
The Advance Radiographic Capability (ARC) at the National Ignition Facility (NIF) is a laser system that employs up to four petawatt (PW) lasers to produce a sequence of short pulses that generate X-rays which backlight high-density inertial confinement fusion (ICF) targets. ARC is designed to produce multiple, sequential X-ray images by using up to eight back lighters. The images will be used to examine the compression and ignition of a cryogenic deuterium-tritium target with tens-of-picosecond temporal resolution during the critical phases of an ICF shot. Multi-frame, hard-X-ray radiography of imploding NIF capsules is a capability which is critical to the success of NIF's missions. As in the NIF system, ARC requires an optical alignment mask that can be inserted and removed as needed for precise positioning of the beam. Due to ARC’s split beam design, inserting the nominal NIF main laser alignment mask in ARC produced a partial blockage of the mask pattern. Requirements for a new mask design were needed. In this paper we describe the ARC mask requirements, the resulting mask design pattern, and the image analysis algorithms used to detect and identify the beam and reference centers required for ARC alignment.
The current automation of image-based alignment of NIF high energy laser beams is providing the capability of executing multiple target shots per day. An important aspect of performing multiple shots in a day is to reduce additional time spent aligning specific beams due to perturbations in those beam images. One such alignment is beam centration through the second and third harmonic generating crystals in the final optics assembly (FOA), which employs two retro-reflecting corner cubes to represent the beam center. The FOA houses the frequency conversion crystals for third harmonic generation as the beams enters the target chamber. Beam-to-beam variations and systematic beam changes over time in the FOA corner-cube images can lead to a reduction in accuracy as well as increased convergence durations for the template based centroid detector. This work presents a systematic approach of maintaining FOA corner cube centroid templates so that stable position estimation is applied thereby leading to fast convergence of alignment control loops. In the matched filtering approach, a template is designed based on most recent images taken in the last 60 days. The results show that new filter reduces the divergence of the position estimation of FOA images.
The Advance Radiographic Capability (ARC) at the National Ignition Facility (NIF) is a laser system that employs up to four petawatt (PW) lasers to produce a sequence of short pulses that generate X-rays which backlight highdensity internal confinement fusion (ICF) targets. Employing up to eight backlighters, ARC can produce an X-ray "motion picture" to diagnose the compression and ignition of a cryogenic deuterium-tritium target with tens-ofpicosecond temporal resolution during the critical phases of an ICF shot. Multi-frame, hard-X-ray radiography of imploding NIF capsules is a capability which is critical to the success of NIF's missions. The function of the Centering and Pointing System (CAPS) in ARC is to provide superimposed near-field and far-field images on a common optical path. The Images are then analyzed to extract beam centering and pointing data for the control system. The images contain the confluence of pointing, centering, and reference patterns. The patterns may have uneven illumination, particularly when the laser is misaligned. In addition, the simultaneous appearance of three reference patterns may be co-incidental, possibly masking one or more of the patterns. Image analysis algorithms have been developed to determine the centering and pointing position of ARC from these images. In the paper we describe the image analysis algorithms used to detect and identify the centers of these patterns. Results are provided, illustrating how well the process meets system requirements.
The Advance Radiographic Capability (ARC) at the National Ignition Facility (NIF) is a laser system designed to produce a sequence of short pulses used to backlight imploding fuel capsules. Laser pulses from a short-pulse oscillator are dispersed in wavelength into long, low-power pulses, injected in the NIF main laser for amplification, and then compressed into high-power pulses before being directed into the NIF target chamber. In the target chamber, the laser pulses hit targets which produce x-rays used to backlight imploding fuel capsules. Compression of the ARC laser pulses is accomplished with a set of precision-surveyed optical gratings mounted inside of vacuum vessels. The tilt of each grating is monitored by a measurement system consisting of a laser diode, camera and crosshair, all mounted in a pedestal outside of the vacuum vessel, and a mirror mounted on the back of a grating inside the vacuum vessel. The crosshair is mounted in front of the camera, and a diffraction pattern is formed when illuminated with the laser diode beam reflected from the mirror. This diffraction pattern contains information related to relative movements between the grating and the pedestal. Image analysis algorithms have been developed to determine the relative movements between the gratings and pedestal. In the paper we elaborate on features in the diffraction pattern, and describe the image analysis algorithms used to monitor grating tilt changes. Experimental results are provided which indicate the high degree of sensitivity provided by the tilt sensor and image analysis algorithms.
The National Ignition Facility (NIF) utilizes 192 beams, four of which are diverted to create the Advanced Radiographic Capability (ARC) by generating a sequence of short laser pulses. This ARC beam after being converted to X-rays will act as a back lighter to create a radiographic movie and provide an unprecedented insight into the imploding dynamics and serve as a diagnostic for tuning the experimental parameters to achieve fusion. One such beam is the centering beam of the pre-amplifier module which due to a split path obstructs the central square alignment fiducials. This fiducial is used for alignment and also as reference for the programmable spatial shaper (PSS) system. Image processing algorithms are used to process the images and calculate the position of various fiducials in the beam path. We discuss the algorithm to process ARC split beam injector (SBI) centering images with partial fiducial information.
A challenging aspect of preparing cryogenic targets for National Ignition Facility (NIF) ignition experiments is growing a single crystal layer (~ 70 m thick) of solid frozen deuterium-tritium (DT) fuel on the inner surface of a spherical hollow plastic capsule 2 mm in diameter. For the most critical fusion experiments, the layer must be smooth, having uniform thickness, and largely free of isolated defects (e.g. grooves). A single target layer typically takes up to 18 hours to form. X-ray images on 3 orthogonal axes are used to monitor the growth of the crystal and evaluate the quality of the layer. While these methods provide a good indicator of target layer condition, new metrics are currently being developed to take advantage of other properties in the x-ray image, which may give earlier indications of target quality. These properties include symmetry of texture, seed formation, and eigenimage analysis. We describe the approach and associated image processing to evaluate and classify these metrics, whose goal is to improve overall layer production and better quantify the quality of the layer during its growth.
Lawrence Livermore National Laboratory is a large, multidisciplinary institution that conducts fundamental
and applied research in the physical sciences. Research programs at the Laboratory run the
gamut from theoretical investigations, to modeling and simulation, to validation through experiment.
Over the years, the Laboratory has developed a substantial research component in the areas of signal
and image processing to support these activities. This paper surveys some of the current research in
signal and image processing at the Laboratory. Of necessity, the paper does not delve deeply into any
one research area, but an extensive citation list is provided for further study of the topics presented.
This paper describes experimental results from a live-fire data collect designed to demonstrate the ability of IR and acoustic sensing systems to detect and map high-volume gunfire events from tactical UAVs. The data collect supports an exploratory study of the FightSight concept in which an autonomous UAV-based sensor exploitation and decision support capability is being proposed to provide dynamic situational awareness for large-scale battalion-level firefights in cluttered urban environments. FightSight integrates IR imagery, acoustic data, and 3D scene context data with prior time information in a multi-level, multi-step probabilistic-based fusion process to reliably locate and map the array of urban firing events and firepower movements and trends associated with the evolving urban battlefield situation. Described here are sensor results from live-fire experiments involving simultaneous firing of multiple sub/super-sonic weapons (2-AK47, 2-M16, 1 Beretta, 1 Mortar, 1 rocket) with high optical and acoustic clutter at ranges up to 400m. Sensor-shooter-target configurations and clutter were designed to simulate UAV sensing conditions for a high-intensity firefight in an urban environment. Sensor systems evaluated were an IR bullet tracking system by Lawrence Livermore National Laboratory (LLNL) and an acoustic gunshot detection system by Planning Systems, Inc. (PSI). The results demonstrate convincingly the ability for the LLNL and PSI sensor systems to accurately detect, separate, and localize multiple shooters and the associated shot directions during a high-intensity firefight (77 rounds in 5 sec) in a high acoustic and optical clutter environment with very low false alarms. Preliminary fusion processing was also examined that demonstrated an ability to distinguish co-located shooters (shooter density), range to <0.5 m accuracy at 400m, and weapon type. The combined results of the high-intensity firefight data collect and a detailed systems study demonstrate the readiness of the FightSight concept for full system development and integration.
With the number of small, inexpensive Unmanned Air Vehicles (UAVs) increasing, it is feasible to build multi-UAV sensing networks. In particular, by using UAVs in conjunction with unattended ground sensors, a degree of persistent sensing can be achieved. With proper UAV cooperation algorithms, sensing is maintained even though exceptional events, e.g., the loss of a UAV, have occurred. In this paper a cooperation architecture is described that allows multiple UAVs to perform coordinated, persistent sensing with unattended ground sensors over a wide area. This architecture automatically adapts the UAV paths so that on the average, the amount of time that any sensor has to wait for a UAV revisit is minimized. We also describe the Simulation, Tactical Operations and Mission Planning (STOMP) software. STOMP is designed to help simulate and operate
distributed sensor networks where multiple UAVs are used to
collect data.
Identification of materials in hyperspectral imagery is a fundamental
analysis task. Materials are often identified by building pixel
models using a library of reference spectra along with a
regression technique. This paper describes several regression
techniques that are useful in modeling hyperspectral pixels,
demonstrates the characteristics of the algorithms on simulated
data, and compares the strengths and weaknesses of the
techniques
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.