This PDF file contains the front matter associated with SPIE Proceedings Volume 9223, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.

Persistent satellite observations are essential for monitoring and understanding Earth’s environmentally sensitive and rapidly changing Arctic region. Compact wide-field-of-view imagers aboard satellites in Highly Elliptical Orbit (HEO) could stare at the Arctic and collect multispectral, high dynamic range visible and near-infrared imagery with sensitivity similar to that of the Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) in sun synchronous polar orbit. These HEO Day/Night Imagers (HDNIs) provide high contrast visible wavelength imagery through the long polar night. Their dynamic range –– extending from the brightest sunlit clouds, ice and snow to reflected moonlight from open water –– enables cloud, ice and sea surface discrimination even under very low light and low thermal contrast conditions. Rapidly refreshed HDNI data results in frequent updates to key environmental products such as cloud imagery and microphysical properties, ice and open water distribution (including real-time maps of where leads are opening and new ice is forming), vector ice motion and vector polar winds from cloud motion. The relatively small size of HDNIs makes them ideal for deployment as a hosted payload or as the primary payload onboard a small satellite.

A primary sensor on-board the Suomi-National Polar-orbiting Partnership (SNPP) spacecraft, the Visible Infrared Imaging Radiometer Suite (VIIRS) has 22 bands: 7 thermal emissive bands (TEBs), 14 reflective solar bands (RSBs) and a Day Night Band (DNB). The RSBs cover the spectral wavelengths between 0.412 to 2.25 μm and have three (I1-I3) 371m and eleven (M1-M11) 742m spatial resolution bands. A VIIRS Key Performance Parameter (KPP) is the Ocean Color/Chlorophyll (OCC) which uses moderate bands M1 (0.412μm) through M7’s (0.865 μm) calibrated Science Data Records (SDRs). The RSB SDRs rely on prelaunch calibration coefficients which use a quadratic algorithm to convert the detector’s response to calibrated radiance. This paper will evaluate the performance of these prelaunch calibration coefficients using SDR comparisons between bands with the same spectral characteristics: I2 with M7 (0.865 μm) and I3 with M10 (1.610 μm). Changes to the prelaunch calibration coefficient’s offset term c0 to improve the SDR’s performance at low radiance levels will also be discussed.

Raytheon’s fourth generation uncooled microbolometer array technology with digital output, High Definition (HD) 1920 × 1200 format and 12 μm cell size enables uncooled thermal infrared (TIR) multispectral imagers with the sensitivity and spatial sampling needed for a variety of Earth observation missions in LEO, GEO and HEO. A powerful combination of small detector cell size, fast optics and high sensitivity achieved without cryogenic cooling leads to instruments that are much smaller than current TIR systems, while still offering the capability to meet challenging measurement requirements for Earth observation missions. To consider how this technology could be implemented for Earth observation missions, we extend our previous studies with visible wavelength CubeSat imagers for environmental observations from LEO and examine whether small thermal infrared imagers based on fourth generation uncooled technology could be made small enough to fit onboard a 3U CubeSat and still meet challenging requirements for legacy missions. We found that moderate spatial resolution (~200 m) high sensitivity cloud and surface temperature observations meeting legacy MODIS/VIIRS requirements could be collected successfully with CubeSat-sized imagers but that multiple imagers are needed to cover the full swath for these missions. Higher spatial resolution land imagers are more challenging to fit into the CubeSat form factor, but it may be possible to do so for systems that require roughly 100 m spatial resolution. Regardless of whether it can fit into a CubeSat or not, uncooled land imagers meeting candidate TIR requirements can be implemented with a much smaller instrument than previous imagers. Even though this technology appears to be very promising, more work is needed to qualify this newly available uncooled infrared technology for use in space. If these new devices prove to be as space worthy as the first generation arrays that Raytheon qualified and built into the THEMIS imager still operating successfully onboard Mars Odyssey 2001, new classes of low cost, uncooled TIR Earth instruments will be enabled that are suitable for use as primary and hosted payloads in LEO, GEO and HEO or in constellations of small satellites as small as CubeSats to support Earth science measurement objectives in weather forecasting, land imaging and climate variability and change.

In order to protect critical military and commercial space assets, the United States Space Surveillance Network must have the ability to positively identify and characterize all space objects. Unfortunately, positive identification and characterization of space objects is a manual and labor intensive process today since even large telescopes cannot provide resolved images of most space objects. The objective of this study was to calibrate a system to exploit the optical signature of unresolved geosynchronous satellite images by collecting polarization data in the visible wavelengths for the purpose of revealing discriminating features. These features may lead to positive identification or classification of each satellite. The system was calibrated with an algorithm and process that takes raw observation data from a two-channel polarimeter and converts it to Stokes parameters S₀ and S₁. This instrumentation is a new asset for the United States Air Force Academy (USAFA) Department of Physics and consists of one 20-inch Ritchey-Chretien telescope and a dual focal plane system fed with a polarizing beam splitter. This study calibrated the system and collected preliminary polarization data on five geosynchronous satellites to validate performance. Preliminary data revealed that each of the five satellites had a different polarization signature that could potentially lead to identification in future studies.

An Electronically Steerable Flash Lidar (ESFL), developed by Ball Aerospace & Technologies Corporation, allows realtime adaptive control of configuration and data-collection strategy based on recent or concurrent observations and changing situations. This paper reviews, at a high level, some of the algorithms and control architecture built into ESFL. Using ESFL as an example, it also discusses the merits and utility such adaptable instruments in Earth-system studies.

Fog conditions are the cause of severe car accidents in western countries because of the poor induced visibility. Its forecast and intensity are still very difficult to predict by weather services. Infrared cameras allow to detect and to identify objects in fog while visibility is too low for eye detection. Over the past years, the implementation of cost effective infrared cameras on some vehicles has enabled such detection. On the other hand pattern recognition algorithms based on Canny filters and Hough transformation are a common tool applied to images. Based on these facts, a joint research program between IFSTTAR and Cerema has been developed to study the benefit of infrared images obtained in a fog tunnel during its natural dissipation. Pattern recognition algorithms have been applied, specifically on road signs which shape is usually associated to a specific meaning (circular for a speed limit, triangle for an alert, …). It has been shown that road signs were detected early enough in images, with respect to images in the visible spectrum, to trigger useful alerts for Advanced Driver Assistance Systems.

Road surface temperature forecast is a key component of winter maintenance strategy in many developed countries. Numerical tools exist to help road managers to organize services and consequently to trigger de-icing operations. Forecasting strategies have been commonplace since the 1980s, and often based on numerical models. Traffic is one of the influencing parameters, specifically in urban areas. This work was undertaken to evaluate to which extent an accurate description of traffic might improve numerical model dedicated to road surface temperature forecasting. Two sets of experiments were run to detect and to quantify traffic effects on RST. First one consisted in driving above an infrared radiometer, a pyrgeometer and other atmospheric probes to measure the radiative contribution of a passing vehicle at various speeds. In the second set, an infrared camera was installed on a vehicle in an urban traffic flow. This camera was mounted on the roof and focused the pavement right behind the vehicle ahead, both circulating at the same speed. Infrared thermography indicated a fleeting contribution of traffic to RST. The temperature increase in circulated areas, with respect to uncirculated ones, does not last according to collected measurements. Measurements with atmospheric and radiometric probes provided elements to properly take into account traffic in a numerical model and to appreciate its contribution.

Acoustic source localization using microphone arrays is widely used in videoconferencing and surveillance systems. However, it still remains a challenging task to develop efficient algorithms for accurate estimation of source location using distributed data processing. In this work, we propose a new algorithm for efficient localization of a speaker in noisy and reverberant environments such as videoconferencing. We propose a hybrid algorithm that combines generalized cross correlation based phase transform method (GCC-PHAT) and Tabu search to obtain a robust and accurate estimate of the speaker location. The Tabu Search algorithm iteratively improves the time difference of arrival (TDOA) estimate of GCC-PHAT by examining the neighboring solutions until a convergence in the TDOA value is obtained. Experiments were performed based on real world data recorded from a meeting room in the presence of noise such as computer and fans. Our results demonstrate that the proposed hybrid algorithm outperforms GCC-PHAT especially when the noise level is high. This shows the robustness of the proposed algorithm in noisy and realistic videoconferencing systems.

An optical positioning sensor is realized by combining optical projection and a virtual camera model. This technique provides a low cost and non-contact measurement and has the potential to provide real-time 6 DOF measurements of an object. The optical sensor first generates a projection pattern that is observable on the surrounding walls, cameras are used to track the motion of the projected optical pattern, and the motion of the optical sensor can thus be tracked indirectly. In this technique, the optical sensor itself is treated as a virtual pinhole camera. A virtual image is generated that carries the angular characteristics of the optical pattern. The virtual image and the images of the projected optical pattern on the walls taken by real cameras are then processed through a photogrammetry-based bundle adjustment to give a position and orientation estimate of the optical sensor. Experiment is performed to calibrate the angular information of the optical pattern. Monte-Carlo simulation is performed to analyse the measurement uncertainty. The simulation result has good agreement with the experimental result for 0.9 m translation test along a precision rail. The optical sensor with the virtual camera model solution is preferable to provide real time measurements for simple-to-complex environments.