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Disclosed is a satellite payload device, including a plurality of sensors configured to detect images of a satellite, a video distribution and storage unit configured to collect, compress, store, and transmit the images, and a control computer configured to request a portion of the plurality of sensors from which to receive sensor data that is routed to the video distribution and storage unit.
Machine learning has been woven into statistics to modernize topic modeling over textual documents written in natural language, and scientific paper search and recommendation can consequently offer higher accuracy instead of counting on traditional keyword-based search. However, topic distribution of a paper resulted from existing topic modeling techniques only relies on the statistics of words contained in the paper itself. We argue that community users’ views of a paper may also provide insights at the time of recommendation. For example, if a paper on fake image detection has been cited heavily by machine learning papers, such a feature should be absorbed in the embedding of this paper, so that it can be recommended for future query on machine learning. In this paper, we present a Graph Convolutional Network-strengthened Topic Modeling (GCN-TM) method, which employs GCN technique to refine topic modeling of scientific papers. A citation-oriented knowledge graph is constructed, and topic modeling is mapped to feature embedding of the comprising papers. On top of its own topics carried in its content, each paper learns topics from its neighbors and revise its embedding accordingly. Our empirical studies over real-life scientific literature has proved the necessity and effectiveness of our proposed approach.
Massive colliding-wind binaries that host a Wolf–Rayet (WR) star present a potentially important source of dust and chemical enrichment in the interstellar medium. However, the chemical composition and survival of dust formed from such systems is not well understood. The carbon-rich Wolf–Rayet binary WR 140 presents an ideal astrophysical laboratory for investigating these questions, given its well-defined orbital period and predictable dust-formation episodes every 7.93 years around periastron passage. We present observations from our Early Release Science programme (ERS 1349) with the James Webb Space Telescope Mid-Infrared Instrument (MIRI) Medium-Resolution Spectrometer and Imager that reveal the spectral and spatial signatures of nested circumstellar dust shells around WR 140. MIRI medium-resolution spectroscopy of the second dust shell and Imager detections of over 17 shells formed throughout approximately the past 130 years confirm the survival of carbonaceous dust grains from WR 140 that are probably carriers of ‘unidentified infrared’-band features at 6.4 and 7.7 μm. The observations indicate that dust-forming carbon-rich Wolf–Rayet binaries can enrich the interstellar medium with organic compounds and carbonaceous dust.
This paper reviews the technique of boundary-layer transition detection using infrared (IR) imaging, emphasizing cryogenic wind-tunnel testing. With the exception of the low-temperature effects on the IR radiation, the discussion is relevant to conventional wind-tunnel and flight testing as well. At low temperatures, IR imaging encounters a reduction in the radiated energy throughout the IR spectrum, combined with a shift to longer wavelengths of the bulk of the radiation. This radiation behavior affects the minimum resolvable temperature difference (MRTD) of the IR imaging system because of its fixed wave band sensitivity. In the absence of commercial long wavelength IR imaging systems, operating at wavelengths longer than 13 micron, some measures can be taken to alleviate the problem caused by the MRTD limitation. The thermal signature of transition can be enhanced by allowing a small and controlled temperature increase of the wind-tunnel flow that induces a transient heat transfer to the model. This action temporarily reveals the model area under the turbulent regime through its higher heating rate compared with the laminar regime. The contrast between the areas exposed to the two regimes can be enhanced by subtraction of thermograms (the equilibrium thermogram from the transient thermogram). Further visual improvement can be obtained through shade stretching or binary shading.
An algorithm to detect fronts in satellite-derived sea surface temperature fields is presented. Although edge detection is the main focus, the problem of cloud detection is also addressed since unidentified clouds can lead to erroneous edge detection. The algorithm relies on a combination of methods and it operates at the picture, the window, and the local level. The resulting edge detection is not based on the absolute strength of the front, but on the relative strength depending on the context, thus, making the edge detection temperature-scale invariant. The performance of this algorithm is shown to be superior to that of simpler algorithms commonly used to locate edges in satellite-derived SST images. This evaluation was performed through a careful comparison between the location of the fronts obtained by applying the various methods to the SST images and the in situ measures of the Gulf Stream position.
For this program, an image profiling system was developed which offers the potential for detecting extremely faint optical sources that are located in close proximity to bright companions. The approach employed is novel in three respects. First, it does not require an optical system wherein extraordinary measures must be taken to minimize diffraction and scatter. Second, it does not require detectors possessing either extreme uniformity in sensitivity or extreme temporal stability. Finally, the system can readily be calibrated, or nulled, in space by testing against an unresolved singular stellar source.
Initial research in this study using theoretical radiation transport models established that the occurrence of a leak is accompanies by a sudden but sustained change in intensity in a given region of an image. In this phase, temporal processing of video images on a frame-by-frame basis was used to detect leaks within a given field of view. The leak detection algorithm developed in this study consists of a digital highpass filter cascaded with a moving average filter. The absolute value of the resulting discrete sequence is then taken and compared to a threshold value to produce the binary leak/no leak decision at each point in the image. Alternatively, averaging over the full frame of the output image produces a single time-varying mean value estimate that is indicative of the intensity and extent of a leak. Laboratory experiments were conducted in which artificially created leaks on a simulated SSME background were produced and recorded from a visible wavelength video camera. This data was processed frame-by-frame over the time interval of interest using an image processor implementation of the leak detection algorithm. In addition, a 20 second video sequence of an actual SSME failure was analyzed using this technique. The resulting output image sequences and plots of the full frame mean value versus time verify the effectiveness of the system.
We developed a fast algorithm to detect hazards on a planetary surface using a single image from a descent spacecraft camera. This is used when landing a mission vehicle.
Coral reefs worldwide are suffering from severe and rapid degradation (Bryant et A, 1998; Hoegh-Guldberg, 1999). Quick, consistent, large-scale assessment is required to assess and monitor their status (e.g., USDOC/NOAA NESDIS et al., 1999). On-going systematic collection of high resolution digital satellite data will exhaustively complement the relatively small number of SPOT, Landsat 4-5, and IRS scenes acquired for coral reefs the last 20 years. The workhorse for current image acquisition is the Landsat 7 ETM+ Long Term Acquisition Plan (Gasch et al. 2000). Coral reefs are encountered in tropical areas and cloud contamination in satellite images is frequently a problem (Benner and Curry 1998), despite new automated techniques of cloud cover avoidance (Gasch and Campana 2000). Fusion of multidate acquisitions is a classical solution to solve the cloud problems. Though elegant, this solution is costly since multiple images must be purchased for one location; the cost may be prohibitive for institutions in developing countries. There are other difficulties associated with fusing multidate images as well. For example, water quality or surface state can significantly change through time in coral reef areas making the bathymetric processing of a mosaiced image strenuous. Therefore, another strategy must be selected to detect clouds and improve coral reefs mapping. Other supplemental data could be helpful and cost-effective for distinguishing clouds and generating the best possible reef maps in the shortest amount of time. Photographs taken from the 1960s to the present from the Space Shuttle and other human-occupied spacecraft are one under-used source of alternative multitemporal data (Lulla et al. 1996). Nearly 400,000 photographs have been acquired during this period, an estimated 28,000 of these taken to date are of potential value for reef remote sensing (Robinson et al. 2000a). The photographic images can be digitized into three bands (red, green and blue) and processed for various applications (e.g., Benner and Curry 1998, Nedeltchev 1999, Glasser and Lulla 2000, Robinson et al. 2000c, Webb et al, in press).
Significant effort and resources are being expended to develop ceramic matrix (CMC), metal matrix (MMC), and polymer matrix (PMC) composites for high-temperature engine components and other parts in advanced aircraft. The objective of this NASA Lewis Research Center study was to evaluate the ability of a thermographic imaging technique for detecting artificially created defects (flat-bottom holes) of various diameters and depths in four composite systems (two CMC's, one MMC, and one PMC) of interest as high-temperature structural materials.
A feasibility study of automated classification of satellite images is described. Satellite images were characterized by the textures they contain. In particular, the detection of cloud textures was investigated. The method of second-order gray level statistics, using co-occurrence matrices, was applied to extract feature vectors from image segments. Neural network technology was employed to classify these feature vectors. The cascade-correlation architecture was successfully used as a classifier. The use of a Kohonen network was also investigated but this architecture could not reliably classify the feature vectors due to the complicated structure of the classification problem. The best results were obtained when data from different spectral bands were fused.
The first full-disk radar images of Mercury are discussed. About 77 percent of the surface was imaged at resolutions as good as 150 km. The North Pole was visible at the time of the observations, and the feature with the highest same sense (SS) circular reflectivity in the images is near the nominal polar position. The peak SS reflectivity of this feature is 7.9 percent and the circular polarization ratio through much of it is over one. The diameter of the feature is estimated to be less than about 350 km. The signal strength and polarization characteristics suggest the presence of very clean ices deposited in a relatively short period. The reflectivity of the north polar feature is slightly depressed compared to absolute reflectivities of other ice bodies and regions in the solar system. This may be due to a layer of dust or soil covering the ice. Other prominent features in the unphotographed hemisphere correpond to positions where atmospheric sodium enhancements have been measured from earth. These sites may be large basins similar to the Caloris basin.
An algorithm processes the pixel intensities of a digitized image to detect and locate a circular bright spot, the approximate size of which is known in advance. The algorithm is used to find images of the Sun in cameras aboard the Mars Exploration Rovers. (The images are used in estimating orientations of the Rovers relative to the direction to the Sun.) The algorithm can also be adapted to tracking of circular shaped bright targets in other diverse applications. The first step in the algorithm is to calculate a dark-current ramp a correction necessitated by the scheme that governs the readout of pixel charges in the charge-coupled-device camera in the original Mars Exploration Rover application. In this scheme, the fraction of each frame period during which dark current is accumulated in a given pixel (and, hence, the dark-current contribution to the pixel image-intensity reading) is proportional to the pixel row number. For the purpose of the algorithm, the dark-current contribution to the intensity reading from each pixel is assumed to equal the average of intensity readings from all pixels in the same row, and the factor of proportionality is estimated on the basis of this assumption. Then the product of the row number and the factor of proportionality is subtracted from the reading from each pixel to obtain a dark-current-corrected intensity reading. The next step in the algorithm is to determine the best location, within the overall image, for a window of N N pixels (where N is an odd number) large enough to contain the bright spot of interest plus a small margin. (In the original application, the overall image contains 1,024 by 1,024 pixels, the image of the Sun is about 22 pixels in diameter, and N is chosen to be 29.)
This paper reports an analysis of polarimetric hyperspectral imagery of an outdoor scene containing a number of inactive mines. The imagery was taken with a visible/near-infrared acousto-optic tunable filter prototype system. The result has illustrated that the characteristics of mine smooth surfaces can create high signals in polarization difference images and consequently provide a quick and reliable approach for detection.
The detection of an optical image in the presence of uniform background light is based on a likelihood ratio formed of the numbers of photoelectrons emitted from small elements of a photoelectric surface onto which the image is focused. When diffraction is negligible and the surface has unit quantum efficiency, this detector is equipollent with the optimum detector of the image forming light. Its performance is compared with that of the threshold detector and that of a detector basing its decisions on the total number of photoelectrons from a finite area of the image. The illuminance of the image is postulated to have a Gaussian spatial distribution. All three detectors exhibit nearly the same reliability.
In recent experiments we have demonstrated the feasibility of a new approach to boundary layer transition detection. This new approach employs the temperature dependence of certain photoluminescent materials in the form of a surface coating or 'paint' to detect the change in heat transfer characteristics that accompany boundary layer transition. The feasibility experiments were conducted for low subsonic to transonic Mach numbers on two-dimensional airfoil and flat plate configurations. Paint derived transition locations were determined and compared to those obtained from Preston pressure probe measurements. Artificial heating of the models was used to obtain transition temperature signatures suitable for the instrumentation available to us. Initial estimates show, however, that passive kinetic heating at high Mach numbers is a promising alternative.
This paper addresses the image reconstruction issues for nulling interferometer arrays and the sensitivities for planet detection.
We report the direct imaging detection of a low-mass companion to a young, moderately active star V450 And, that was previously identified with the radial velocity (RV) method. The companion was found in high-contrast images obtained with the Subaru Telescope equipped with the HiCIAO camera and AO188 adaptive optics system. From the public ELODIE and SOPHIE archives we extracted available high-resolution spectra and RV measurements, along with RVs from the Lick planet search program. We combined our multi-epoch astrometry with these archival, partially unpublished RVs, and found that the companion is a low-mass star, not a brown dwarf, as previously suggested. We found the best-fitting dynamical masses to be m(sub 1) = 1.141(sup + 0.037, sub - 0.091) and m(sub 2) = 0.279(sup + 0.023, sub - 0.020) solar mass. We also performed spectral analysis of the SOPHIE spectra with the iSpec code. Hipparcos time-series photometry shows a periodicity of P = 5.743 day, which is also seen in the SOPHIE spectra as an RV modulation of the star A. We interpret it as being caused by spots on the stellar surface, and the star to be rotating with the given period. From the rotation and level of activity, we found that the system is 380(sup + 220, sub -100) Myr old, consistent with an isochrone analysis (220 (sup +2120, sub -90) Myr). This work may serve as a test case for future studies of low mass stars, brown dwarfs, and exoplanets by combination of RV and direct imaging data.