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Maximum Likelihood Convolutional Decoding, which is used by the Deep Space Network for short constraint-length convolutional codes, assumes that all strings of information bits are equally likely. In some cases, like image data, this is not the case. The use of information about an adjacent pixel in decoding convolutionally encoded Voyager images is examined. It is discovered that, in a region of interest, as much as 2 dB may be gained.
Registration techniques play a central role in applications of image processing to computer vision, medical imaging, and automatic target tracking. Feature-based techniques such as scale-invariant feature transform (SIFT) and speeded up robust features (SURF) are commonly used to register images derived from a single modality. However, SIFT and SURF struggle to register images from different modalities because the features tend to manifest rather differently and at sometimes very different length-scales. The most successful methods that have been developed to register multi-modal data use information-theoretic approaches. These methods play a key part in nondestructive evaluation scenarios where data that is collected by sensors of different modalities must be registered to be fused. In this paper, automated registration based on normalized mutual information is applied to align data derived from ultrasonic and radiographic inspections of (i) additively manufactured titanium alloy test coupons, and (ii) thin, lithium metal pouch-cell batteries. The quality of the registration is quantified in terms of computational resources and spatial accuracy. In the first case the X-ray computed tomography (XCT) data is captured on a region corresponding to a small subset of the ultrasonic data, while in the case of the lithium batteries the digital radiography (DR) captures a larger region of interest than the ultrasonic data. In both cases the radiographic data resolution is much higher than for ultrasound, but interestingly, in both cases the accuracy of the registration is approximately equal to two-to-three-pixel lengths in the ultrasonic images.
A solid-state digital camera was developed for obtaining H alpha images of solar flares with 0.1 s time resolution. Beginning in the summer of 1988, this system will be operated in conjunction with SMM's hard X-ray burst spectrometer (HXRBS). Important electron time-of-flight effects that are crucial for determining the flare energy release processes should be detectable with these combined H alpha and hard X-ray observations. Charge-injection device (CID) cameras provide 128 x 128 pixel images simultaneously in the H alpha blue wing, line center, and red wing, or other wavelength of interest. The data recording system employs a microprocessor-controlled, electronic interface between each camera and a digital processor board that encodes the data into a serial bitstream for continuous recording by a standard video cassette recorder. Only a small fraction of the data will be permanently archived through utilization of a direct memory access interface onto a VAX-750 computer. In addition to correlations with hard X-ray data, observations from the high speed H alpha camera will also be correlated and optical and microwave data and data from future MAX 1991 campaigns. Whether the recorded optical flashes are simultaneous with X-ray peaks to within 0.1 s, are delayed by tenths of seconds or are even undetectable, the results will have implications on the validity of both thermal and nonthermal models of hard X-ray production.
The expanded remotely sensed data space consisting of coincident radar backscatter and optical reflectance data provides for a more complete description of the Earth surface. This is especially useful where many parameters are needed to describe a certain scene, such as in the presence of dense and complex-structured vegetation or where there is considerable underlying topography. The goal of this paper is to use a combination of radar and optical data to develop a methodology for parameter classification for dense and hilly forests, and further, class-specific parameter estimation. The area to be used in this study is the H. J. Andrews Forest in Oregon, one of the Long-Term Ecological Research (LTER) sites in the US. This area consists of various dense old-growth conifer stands, and contains significant topographic relief. The Andrews forest has been the subject of many ecological studies over several decades, resulting in an abundance of ground measurements. Recently, biomass and leaf-area index (LAI) values for approximately 30 reference stands have also become available which span a large range of those parameters. The remote sensing data types to be used are the C-, L-, and P-band polarimetric radar data from the JPL airborne SAR (AIRSAR), the C-band single-polarization data from the JPL topographic SAR (TOPSAR), and the Thematic Mapper (TM) data from Landsat, all acquired in late April 1998. The total number of useful independent data channels from the AIRSAR is 15 (three frequencies, each with three unique polarizations and amplitude and phase of the like-polarized correlation), from the TOPSAR is 2 (amplitude and phase of the interferometric correlation), and from the TM is 6 (the thermal band is not used). The range pixel spacing of the AIRSAR is 3.3m for C- and L-bands and 6.6m for P-band. The TOPSAR pixel spacing is 10m, and the TM pixel size is 30m. To achieve parameter classification, first a number of parameters are defined which are of interest to ecologists for forest process modeling. These parameters include total biomass, leaf biomass, LAI, and tree height. The remote sensing data from radar and TM are used to formulate a multivariate analysis problem given the ground measurements of the parameters. Each class of each parameter is defined by a probability density function (pdf), the spread of which defines the range of that class. High classification accuracy results from situations in which little overlap occurs between pdfs. Classification results provide the basis for the future work of class-specific parameter estimation using radar and optical data. This work was performed in part by the Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, and in part by the NASA Ames Research Center, Moffett Field, CA, both under contract from the National Aeronautics and Space Administration.
In evaluating GOES-R Advanced Baseline Imager (ABI) image navigation quality, upsampled sub-images of ABI images are translated against downsampled Landsat 8 images of localized, high contrast earth scenes to determine the translations in the East-West and North-South directions that provide maximum correlation. The native Landsat resolution is much finer than that of ABI, and Landsat navigation accuracy is much better than ABI required navigation accuracy and expected performance. Therefore, Landsat images are considered to provide ground truth for comparison with ABI images, and the translations of ABI sub-images that produce maximum correlation with Landsat localized images are interpreted as ABI navigation errors. The measured local navigation errors from registration of numerous sub-images with the Landsat images are averaged to provide a statistically reliable measurement of the overall navigation error of the ABI image. The dispersion of the local navigation errors is also of great interest, since ABI navigation requirements are specified as bounds on the 99.73rd percentile of the magnitudes of per pixel navigation errors. However, the measurement uncertainty inherent in the use of image registration techniques tends to broaden the dispersion in measured local navigation errors, masking the true navigation performance of the ABI system. We have devised a novel and simple method for estimating the magnitude of the measurement uncertainty in registration error for any pair of images of the same earth scene. We use these measurement uncertainty estimates to filter out the higher quality measurements of local navigation error for inclusion in statistics. In so doing, we substantially reduce the dispersion in measured local navigation errors, thereby better approximating the true navigation performance of the ABI system.
This document describes the results of the fourth pixel-level transit injection experiment, which was designed to measure the detection efficiency of both the Kepler pipeline (Jenkins 2002, 2010; Jenkins et al. 2017) and the Robovetter (Coughlin 2017). Previous transit injection experiments are described in Christiansen et al. (2013, 2015a,b, 2016).In order to calculate planet occurrence rates using a given Kepler planet catalogue, produced with a given version of the Kepler pipeline, we need to know the detection efficiency of that pipeline. This can be empirically determined by injecting a suite of simulated transit signals into the Kepler data, processing the data through the pipeline, and examining the distribution of successfully recovered transits. This document describes the results for the pixel-level transit injection experiment performed to accompany the final Q1-Q17 Data Release 25 (DR25) catalogue (Thompson et al. 2017)of the Kepler Objects of Interest. The catalogue was generated using the SOC pipeline version 9.3 and the DR25 Robovetter acting on the uniformly processed Q1-Q17 DR25 light curves (Thompson et al. 2016a) and assuming the Q1-Q17 DR25 Kepler stellar properties (Mathur et al. 2017).
The development of a preprocessing unit for Landsat Thematic Mapper (TM) data for the Earth Observations Data Laboratory at Johnson Space Center is reported. The background of the project is sketched, including the greatly increased data-handling requirements compared to MSS, the influence of the JPL VICAR system on the system design, and the completeness of the GSFC SCROUNGE (LASLIB) TM data tapes. The design approach and realization are discussed, and the performance and transportability of the preprocessor programs (totaling about 2000 lines of source code in FORTRAN and IBM Assembly languages) are indicated. The system is able to read the TM image tapes, extract areas of interest to particular studies, and register the extracted imagery to suitable references. Ancillary programs include image enhancement, rotation, filtering and pixel-size modification.
Hyperspectral images have many bands requiring significant computational power for machine interpretation. During image pre-processing, regions of interest that warrant full examination need to be identified quickly. One technique for speeding up the processing is to use only a small subset of bands to determine the 'interesting' regions. The problem addressed here is how to determine the fewest bands required to achieve a specified performance goal for pixel classification. The band selection problem has been addressed previously Chen et al., Ghassemian et al., Henderson et al., and Kim et al.. Some popular techniques for reducing the dimensionality of a feature space, such as principal components analysis, reduce dimensionality by computing new features that are linear combinations of the original features. However, such approaches require measuring and processing all the available bands before the dimensionality is reduced. Our approach, adapted from previous multidimensional signal analysis research, is simpler and achieves dimensionality reduction by selecting bands. Feature selection algorithms are used to determine which combination of bands has the lowest probability of pixel misclassification. Two elements required by this approach are a choice of objective function and a choice of search strategy.
Humans will soon be returning to the surface of the Moon with NASA’s Artemis program. The Artemis program is an international collaboration that will consist of a complex series of space systems and missions to explore the lunar surface and pave the way for the future exploration of Mars. NASA and its partners rely heavily on simulation for lighting and navigation studies as well as training astronauts, flight controllers, and mission support staff. The NASA Exploration Systems Simulations (NExSyS) team in the Simulation and Graphics Branch (ER7) in the Engineering Directorate at NASA’s Johnson Space Center has built up many simulation products to support this effort, one of which is the Digital Lunar Exploration Sites (DLES). DLES is a collection of products used to simulate and render the lunar surface in a digital environment. We discussed and presented an overview of the DLES products at the 2022 IEEE Aerospace Conference in Big Sky, MT with a paper titled "Digital Lunar Exploration Sites". This “DLES Terrain Crafting” paper will expand on the information previously provided in “DLES” paper and dive deeper into the details of the terrain crafting process and the toolsets used to support this task. The best digital data currently available of the lunar surface is provided by the Lunar Reconnaissance Orbiter (LRO). Its Lunar Orbiter Laser Altimeter (LOLA) achieves an impressive resolution of 5m per pixel at the Lunar South Pole (LSP) and can generate datasets covering a large continuous region near the LSP. There are a few additional methods, such as Shape from Shading which can infer higher resolution data (up to 1m per pixel) from the LRO Narrow Angle Camera (NAC) images. However, surface-based simulations require higher-resolution data, and this paper will discuss the process of enhancing the terrain to meet that need. The process begins with capturing statistical data of craters in the regions of interest using images provided by the LRO NAC. This data is then used to scatter artificial features which are not captured in the truth data, resulting in an enhanced DEM with a much higher resolution of 20cm per pixel. Many tools were built up to assist in the creation of these artificial Digital Elevation Models (DEM), which this paper will discuss in detail. DEMs themselves are a very powerful representation of a planetary surface, and many operations and tools can utilize the data they contain. This paper includes a description of the rendering of the lunar surface in a graphics engine, generation of contact patches to simulate tire to ground interaction, and ray tracing utilities to model Line of Sight (LOS) interactions with the terrain. This paper will also explore some new tool sets currently under development which aim to utilize Machine Learning (ML) to assist in the identification of craters from LRO NAC imagery. While this is not a novel idea, the NExSyS team is developing a unique approach which may result in more robust identification of crater characteristics.
Three Landsat MSS scenes were processed to empirically determine the sampling intensity needed to characterize various land cover types including water, conifer, and hardwood. The block sizes used as the sampling units were 497 by 500 pixels (picture elements) and 248 by 250 pixels. It is found that, for a given accuracy criterion, the sampling intensity is dependent on the abundance of the cover type of interest in the MSS scene. The results also indicate that, when using the smaller block size, a smaller percentage of the scene has to be classified to obtain a given level of accuracy.
The ambient-light-canceling camera (ALCC) is a proposed near-infrared electronic camera that would utilize a combination of (1) synchronized illumination during alternate frame periods and (2) subtraction of readouts from consecutive frames to obtain images without a background component of ambient light. The ALCC is intended especially for use in tracking the motion of an eye by the pupil center corneal reflection (PCCR) method. Eye tracking by the PCCR method has shown potential for application in human-computer interaction for people with and without disabilities, and for noninvasive monitoring, detection, and even diagnosis of physiological and neurological deficiencies. In the PCCR method, an eye is illuminated by near-infrared light from a lightemitting diode (LED). Some of the infrared light is reflected from the surface of the cornea. Some of the infrared light enters the eye through the pupil and is reflected from back of the eye out through the pupil a phenomenon commonly observed as the red-eye effect in flash photography. An electronic camera is oriented to image the user's eye. The output of the camera is digitized and processed by algorithms that locate the two reflections. Then from the locations of the centers of the two reflections, the direction of gaze is computed. As described thus far, the PCCR method is susceptible to errors caused by reflections of ambient light. Although a near-infrared band-pass optical filter can be used to discriminate against ambient light, some sources of ambient light have enough in-band power to compete with the LED signal. The mode of operation of the ALCC would complement or supplant spectral filtering by providing more nearly complete cancellation of the effect of ambient light. In the operation of the ALCC, a near-infrared LED would be pulsed on during one camera frame period and off during the next frame period. Thus, the scene would be illuminated by both the LED (signal) light and the ambient (background) light during one frame period, and would be illuminated with only ambient (background) light during the next frame period. The camera output would be digitized and sent to a computer, wherein the pixel values of the background-only frame would be subtracted from the pixel values of the signal-plus-background frame to obtain signal-only pixel values (see figure). To prevent artifacts of motion from entering the images, it would be necessary to acquire image data at a rate greater than the standard video rate of 30 frames per second. For this purpose, the ALCC would exploit a novel control technique developed at NASA s Jet Propulsion Laboratory for advanced charge-coupled-device (CCD) cameras. This technique provides for readout from a subwindow [region of interest (ROI)] within the image frame. Because the desired reflections from the eye would typically occupy a small fraction of the area within the image frame, the ROI capability would make it possible to acquire and subtract pixel values at rates of several hundred frames per second considerably greater than the standard video rate and sufficient to both (1) suppress motion artifacts and (2) track the motion of the eye between consecutive subtractive frame pairs.
Commercially available cameras are not design for the combination of single frame and high-speed streaming digital video with real-time control of size and location of multiple regions-of-interest (ROI). A new control paradigm is defined to eliminate the tight coupling between the camera logic and the host controller. This functionality is achieved by defining the indivisible pixel read out operation on a per ROI basis with in-camera time keeping capability. This methodology provides a Random Access, Real-Time, Event-driven (RARE) camera for adaptive camera control and is will suited for target tracking applications requiring autonomous control of multiple ROI's. This methodology additionally provides for reduced ROI read out time and higher frame rates compared to the original architecture by avoiding external control intervention during the ROI read out process.
Photoelectric X-ray polarimeters based on pixel micropattern gas detectors (MPGDs) offer order-of-magnitude improvement in sensitivity over more traditional techniques based on X-ray scattering. This new technique places some of the most interesting astronomical observations within reach of even a small, dedicated mission. The most sensitive instrument would be a photoelectric polarimeter at the focus of 2 a very large mirror, such as the planned XEUS. Our efforts are focused on a smaller pathfinder mission, which would achieve its greatest sensitivity with large-area, low-background, collimated polarimeters. We have recently demonstrated a MPGD polarimeter using amorphous silicon thin-film transistor (TFT) readout suitable for the focal plane of an X-ray telescope. All the technologies used in the demonstration polarimeter are scalable to the areas required for a high-sensitivity collimated polarimeter. Leywords: X-ray polarimetry, particle tracking, proportional counter, GEM, pixel readout
Various instrumental or geophysical artifacts, such as saturation, stray light, or obstruction of light (either coming from the instrument or related to solar eclipses), negatively impact satellite measured ultraviolet and visible Earthshine radiance spectra and downstream retrievals of atmospheric and surface properties derived from these spectra. In addition, excessive noise such as from cosmic ray impacts, prevalent within the South Atlantic Anomaly, can also degrade satellite radiance measurements. Saturation specifically pertains to observations of very bright surfaces such as sun glint over open water or thick clouds. When saturation occurs, additional photoelectric charge generated at the saturated pixel may overflow to pixels adjacent to a saturated area and be reflected as a distorted image in the final sensor output.When these effects cannot be corrected to an acceptable level for science quality retrievals, flagging of the affected pixels is indicated. Here, we introduce a straightforward detection method that is based on the correlation, r, between the observed Earthshine radiance and solar irradiance spectraover a 10 nm-spectral range; our Decorrelation Index (DI for brevity) is simply defined as DI=1-r. DI increases with anomalous additive effects or excessive noise in either radiances, the most likely cause indata from theOzone Monitoring Instrument (OMI),or irradiances. DI is relatively straight-forward to use and interpret and can be 20computed for different wavelength intervals. We developed a set of DIs for two spectral channelsof the OMI, a hyperspectral pushbroom imaging spectrometer. For each OMI spatial measurement, we define 14 wavelength-dependent DIs within the OMI visible channel (350-498 nm) and 6 DIs in its ultraviolet 2 (UV2) channel (310-370 nm). As defined, DIs reflect a continuous range of deviations of observed spectra from the reference irradiance spectrum that are complementary to the binary Saturation Possibility Warning (SPW) flags currently provided for each individual spectral/spatial pixel in the OMI radiance data set. Smaller values of DI are also caused by a number of geophysical factors; this allows one to obtain interesting physical results on the global distribution of spectral variations.
Silicon array readouts for microchannel plate intensifiers offer several attractive features. In this class of detector, the electron cloud output of the MCP intensifier is converted to visible light by a phosphor; that light is then fiber-optically coupled to the silicon array. In photon-counting mode, the resulting light splashes on the silicon array are recognized and centroided to fractional pixel accuracy by off-chip electronics. This process can result in very high (MCP-limited) spatial resolution while operating at a modest MCP gain (desirable for dynamic range and long term stability). The principal limitation of intensified CCD systems of this type is their severely limited local dynamic range, as accurate photon counting is achieved only if there are not overlapping event splashes within the frame time of the device. This problem can be ameliorated somewhat by processing events only in pre-selected windows of interest of by using an addressable charge injection device (CID) for the readout array. We are currently pursuing the development of an intriguing alternative readout concept based on using an event-driven CMOS Active Pixel Sensor. APS technology permits the incorporation of discriminator circuitry within each pixel. When coupled with suitable CMOS logic outside the array area, the discriminator circuitry can be used to trigger the readout of small sub-array windows only when and where an event splash has been detected, completely eliminating the local dynamic range problem, while achieving a high global count rate capability and maintaining high spatial resolution. We elaborate on this concept and present our progress toward implementing an event-driven APS readout.
Silicon array readouts for microchannel plate intensifiers offer several attractive features. In this class of detector, the electron cloud output of the MCP intensifier is converted to visible light by a phosphor; that light is then fiber-optically coupled to the silicon array. In photon-counting mode, the resulting light splashes on the silicon array are recognized and centroided to fractional pixel accuracy by off-chip electronics. This process can result in very high (MCP-limited) spatial resolution for the readout while operating at a modest MCP gain (desirable for dynamic range and long term stability). The principal limitation of intensified CCD systems of this type is their severely limited local dynamic range, as accurate photon counting is achieved only if there are not overlapping event splashes within the frame time of the device. This problem can be ameliorated somewhat by processing events only in pre-selected windows of interest or by using an addressable charge injection device (CID) for the readout array. We are currently pursuing the development of an intriguing alternative readout concept based on using an event-driven CMOS Active Pixel Sensor. APS technology permits the incorporation of discriminator circuitry within each pixel. When coupled with suitable CMOS logic outside the array area, the discriminator circuitry can be used to trigger the readout of small sub-array windows only when and where an event splash has been detected, completely eliminating the local dynamic range problem, while achieving a high global count rate capability and maintaining high spatial resolution. We elaborate on this concept and present our progress toward implementing an event-driven APS readout.
The problem: satellite swath overlaps. Polar orbiting satellites like Terra, Aqua, and the joint NASA/NOAA Suomi-NPP satellite circle the globe from pole to pole, collecting data daily, swath by swath. Having this density of data is great for building a comprehensive mosaic of the planet, but sometimes there is something interesting occurring in one swath but is covered by a subsequent swath when the satellite passes over 90 minutes later. With our new prototype based on Worldview and the Global Imagery Browse Services (GIBS), we combine the best of both worlds to interactively visualize the entire globe as a mosaic and allow the user to _peel away the overlaps_ to see every pixel that was observed by the satellite. This ability to look at every pixel - and to know when they were captured - is especially important near the poles where swath overlaps are most common.
The Very Large Array (VLA) was used in September 1989 to search Comet P/Brorsen-Metcalf (1989o) for the 1 sub 11-1 sub 10 transition of formaldehyde (H2CO) at 4,829.659 MHz and for the J equals 1-0, F equals 2-1 rotational transition of cyanoacetylene (HC3N) at 9098.3321 MHz. A new technique was used in reducing the data. Data blocks which were either 3x3 pixels, 5x5 pixels, or 9x9 pixels were examined for a signal from H2CO. Using this approach, different pixel clusters within the field of view can be sampled to optimize the coupling of the synthesized beam to the gas distribution. HC3N is of immediate interest as a cometary molecule because it may be a reservoir of carbon and a source of cometary CN. Our search for HC3N emission at 3.3 cm wavelength demonstrated that for this molecule the VLA can be expected to reach significant levels of sensitivity in many comets.