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All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Method and System for Temporal Filtering in Video Compression Systems

Three related innovations combine improved non-linear motion estimation, video coding, and video compression. The first system comprises a method in which side information is generated using an adaptive, non-linear motion model. This method enables extrapolating and interpolating a visual signal, including determining the first motion vector between the first pixel position in a first image to a second pixel position in a second image; determining a second motion vector between the second pixel position in the second image and a third pixel position in a third image; determining a third motion vector between the first pixel position in the first image and the second pixel position in the second image, the second pixel position in the second image, and the third pixel position in the third image using a non-linear model; and determining a position of the fourth pixel in a fourth image based upon the third motion vector. For the video compression element, the video encoder has low computational complexity and high compression efficiency. The disclosed system comprises a video encoder and a decoder. The encoder converts the source frame into a space-frequency representation, estimates the conditional statistics of at least one vector of space-frequency coefficients with similar frequencies, and is conditioned on previously encoded data. It estimates an encoding rate based on the conditional statistics and applies a Slepian-Wolf code with the computed encoding rate. The method for decoding includes generating a side-information vector of frequency coefficients based on previously decoded source data and encoder statistics and previous reconstructions of the source frequency vector. It also performs Slepian-Wolf decoding of a source frequency vector based on the generated side-information and the Slepian-Wolf code bits. The video coding element includes receiving a first reference frame having a first pixel value at a first pixel position, a second reference frame having a second pixel value at a second pixel position, and a third reference frame having a third pixel value at a third pixel position. It determines a first motion vector between the first pixel position and the second pixel position, a second motion vector between the second pixel position and the third pixel position, and a fourth pixel value for a fourth frame based upon a linear or nonlinear combination of the first pixel value, the second pixel value, and the third pixel value. A stationary filtering process determines the estimated pixel values. The parameters of the filter may be predetermined constants.

Lu, Ligang↗
NASA NTRSDate not supplied

Determining True Sensor Spatial Resolution of Very High Resolution Optical Imagery

Some satellite data is delivered in images with gridded pixels. This gridded pixel size is often assumed to be the spatial resolution of the satellite sensor; however, this is not always the case. An image can be grided to any arbitrary pixel size, but the sensor resolution will remain constant. For example, an image with a pixel grid size much smaller than the sensor resolution will appear blurry along what should be sharp transitions. This discrepancy between an image’s pixel size and true sensor spatial resolution can be the source of much confusion and even misinformation among data users, which may lead them to waste time and resources on using images that do not suit their spatial resolution needs. This presentation will highlight our evaluation of the true spatial resolution of various government and commercial images in the pixel size range of 0.3 m to 60 m. Images evaluated include ESA’s Sentinel-2 (60 m, 20 m, 10 m pixels), USGS’s Landsat 8/9 (30 m & 15 m pixels), Planet’s SuperDoves (3 m pixels), BlackSky’s Globals (~1 m pixels), and the optical bands of Maxar’s WorldView-2 (2.4 m – 0.41 m pixels) and WorldView-3 (1.38 m – 0.31 m pixels). Our evaluation of true sensor spatial resolution, or ‘footprint size’ is based on the sensor’s line spread function (LSF). We calculate the width at half the height of the LSF to find the full width at half maximum (FWHM). The FWHM is how we report sensor spatial resolution. Different objects are examined for constructing the LSF depending on the sensor spatial resolution. Coarser resolution sensors in this evaluation such as Sentinel-2 and Landsat 8/9 are examined at bridges over a dark water background. The bright bridge acts as a line impulse, giving a sensor’s line spread function (LSF) in one direction. Additionally, we simulate the impacts of bridge width on the apparent LSF to obtain a true LSF without the effects of bridge width for these sensors. Finer resolution sensors will image the irregularities in bridges such as trusses, sidewalks, and in some cases painted lines, interfering with the LSF construction. Instead, these sensors are evaluated at large (60 m – 140 m) black and white checkerboards known as Cal/Val sites. At these locations, the image’s transition from black to white is extracted as an edge spread function (ESF). We calculate the derivative of this ESF to obtain the sensor’s LSF. From there, we find the FWHM as we do for the coarser resolution images. With the FWHM and pixel size, we determine how over- or under-sampled the images are. When the ratio of a sensor’s spatial resolution and the gridded image’s pixel size is less than 1, the image is considered under-sampled. In this case, each pixel’s information is unique but only a portion of that pixel’s ground area has been measured. On the other side, if the ratio is greater than 1, the image is considered over-sampled. That is, each pixel’s information is sourced from within the ground extent of the pixel and some extent outside additionally. We will show the true spatial resolution and the extent of over-/under-sampling in the imagery from ESA’s Sentinel-2 (60 m, 20 m, 10 m pixels), USGS’s Landsat 8/9 (30 m & 15 m pixels), Planet’s SuperDoves (3 m pixels), BlackSky’s Globals (~1 m pixels), and the optical bands of Maxar’s WorldView-2 (2.4 m – 0.41 m pixels) and WorldView-3 (1.38 m – 0.31 m pixels).

Alana Semple↗
NASA NTRS2005

Single Particle Damage Events in Candidate Star Camera Sensors

Si charge coupled devices (CCDs) are currently the preeminent detector in star cameras as well as in the near ultraviolet (uv) to visible wavelength region for astronomical observations in space and in earth-observing space missions. Unfortunately, the performance of CCDs is permanently degraded by total ionizing dose (TID) and displacement damage effects. TID produces threshold voltage shifts on the CCD gates and displacement damage reduces the charge transfer efficiency (CTE), increases the dark current, produces dark current nonuniformities and creates random telegraph noise in individual pixels. In addition to these long term effects, cosmic ray and trapped proton transients also interfere with device operation on orbit. In the present paper, we investigate the dark current behavior of CCDs - in particular the formation and annealing of hot pixels. Such pixels degrade the ability of a CCD to perform science and also can present problems to the performance of star camera functions (especially if their numbers are not correctly anticipated). To date, most dark current radiation studies have been performed by irradiating the CCDs at room temperature but this can result in a significantly optimistic picture of the hot pixel count. We know from the Hubble Space Telescope (HST) that high dark current pixels (so-called hot pixels or hot spikes) accumulate as a function of time on orbit. For example, the HST Advanced Camera for Surveys/Wide Field Camera instrument performs monthly anneals despite the loss of observational time, in order to partially anneal the hot pixels. Note that the fact that significant reduction in hot pixel populations occurs for room temperature anneals is not presently understood since none of the commonly expected defects in Si (e.g. divacancy, E center, and A-center) anneal at such a low temperature. A HST Wide Field Camera 3 (WFC3) CCD manufactured by E2V was irradiated while operating at -83C and the dark current studied as a function of temperature while the CCD was warmed to a sequence of temperatures up to a maximum of +30C. The device was then cooled back down to -83 and re-measured. Hot pixel populations were tracked during the warm-up and cool-down. Hot pixel annealing began below 40C and the anneal process was largely completed before the detector reached +3OC. There was no apparent sharp temperature dependence in the annealing. Although a large fraction of the hot pixels fell below the threshold to be counted as a hot pixel, they nevertheless remained warmer than the remaining population. The details of the mechanism for the formation and annealing of hot pixels is not presently understood, but it appears likely that hot pixels are associated with displacement damage occurring in high electric field regions.

Marshall, Paul↗
NASA NTRS2009

Hardware Implementation of a Bilateral Subtraction Filter

A bilateral subtraction filter has been implemented as a hardware module in the form of a field-programmable gate array (FPGA). In general, a bilateral subtraction filter is a key subsystem of a high-quality stereoscopic machine vision system that utilizes images that are large and/or dense. Bilateral subtraction filters have been implemented in software on general-purpose computers, but the processing speeds attainable in this way even on computers containing the fastest processors are insufficient for real-time applications. The present FPGA bilateral subtraction filter is intended to accelerate processing to real-time speed and to be a prototype of a link in a stereoscopic-machine- vision processing chain, now under development, that would process large and/or dense images in real time and would be implemented in an FPGA. In terms that are necessarily oversimplified for the sake of brevity, a bilateral subtraction filter is a smoothing, edge-preserving filter for suppressing low-frequency noise. The filter operation amounts to replacing the value for each pixel with a weighted average of the values of that pixel and the neighboring pixels in a predefined neighborhood or window (e.g., a 9 9 window). The filter weights depend partly on pixel values and partly on the window size. The present FPGA implementation of a bilateral subtraction filter utilizes a 9 9 window. This implementation was designed to take advantage of the ability to do many of the component computations in parallel pipelines to enable processing of image data at the rate at which they are generated. The filter can be considered to be divided into the following parts (see figure): a) An image pixel pipeline with a 9 9- pixel window generator, b) An array of processing elements; c) An adder tree; d) A smoothing-and-delaying unit; and e) A subtraction unit. After each 9 9 window is created, the affected pixel data are fed to the processing elements. Each processing element is fed the pixel value for its position in the window as well as the pixel value for the central pixel of the window. The absolute difference between these two pixel values is calculated and used as an address in a lookup table. Each processing element has a lookup table, unique for its position in the window, containing the weight coefficients for the Gaussian function for that position. The pixel value is multiplied by the weight, and the outputs of the processing element are the weight and pixel-value weight product. The products and weights are fed to the adder tree. The sum of the products and the sum of the weights are fed to the divider, which computes the sum of products the sum of weights. The output of the divider is denoted the bilateral smoothed image. The smoothing function is a simple weighted average computed over a 3 3 subwindow centered in the 9 9 window. After smoothing, the image is delayed by an additional amount of time needed to match the processing time for computing the bilateral smoothed image. The bilateral smoothed image is then subtracted from the 3 3 smoothed image to produce the final output. The prototype filter as implemented in a commercially available FPGA processes one pixel per clock cycle. Operation at a clock speed of 66 MHz has been demonstrated, and results of a static timing analysis have been interpreted as suggesting that the clock speed could be increased to as much as 100 MHz.

Huertas, Andres↗
NASA NTRS2011

Validating Phasing and Geometry of Large Focal Plane Arrays

The Kepler Mission is designed to survey our region of the Milky Way galaxy to discover hundreds of Earth-sized and smaller planets in or near the habitable zone. The Kepler photometer is an array of 42 CCDs (charge-coupled devices) in the focal plane of a 95-cm Schmidt camera onboard the Kepler spacecraft. Each 50x25-mm CCD has 2,200 x 1,024 pixels. The CCDs accumulate photons and are read out every six seconds to prevent saturation. The data is integrated for 30 minutes, and then the pixel data is transferred to onboard storage. The data is subsequently encoded and transmitted to the ground. During End-to-End Information System (EEIS) testing of the Kepler Mission System (KMS), there was a need to verify that the pixels requested by the science team operationally were correctly collected, encoded, compressed, stored, and transmitted by the FS, and subsequently received, decoded, uncompressed, and displayed by the Ground Segment (GS) without the outputs of any CCD modules being flipped, mirrored, or otherwise corrupted during the extensive FS and GS processing. This would normally be done by projecting an image on the focal plane array (FPA), collecting the data in a flight-like way, and making a comparison between the original data and the data reconstructed by the science data system. Projecting a focused image onto the FPA through the telescope would normally involve using a collimator suspended over the telescope opening. There were several problems with this approach: the collimation equipment is elaborate and expensive; as conceived, it could only illuminate a limited section of the FPA (.25 percent) during a given test; the telescope cover would have to be deployed during testing to allow the image to be projected into the telescope; the equipment was bulky and difficult to situate in temperature-controlled environments; and given all the above, test setup, execution, and repeatability were significant concerns. Instead of using this complicated approach of projecting an optical image on the FPA, the Kepler project developed a method using known defect features in the CCDs to verify proper collection and reassembly of the pixels, thereby avoiding the costs and risks of the optical projection approach. The CCDs composing the Kepler FPA, as all CCDs, had minor defects. At ambient temperature, some pixels look far brighter than they should. These ghot h pixels have a higher rate of charge leakage than the others due to manufacturing variations. They are usually stable over time, and appear at temperatures above 5 oC. The hot pixels on the Kepler FPA were mapped before photometer assembly during module testing. Selected hot pixels were used as target gstars h for the purposes of EEIS testing. gDead h pixels are permanently off, producing a permanently black pixel. These can also be used if there is some illumination of the FPA. During EEIS testing, Dark Current Full Frame Images (FFIs) taken at room temperature were used to create the hot pixel maps for all 84 Kepler photometer CCD channels. Data from two separate nights were used to create two hot pixel maps per channel, which were cross-correlated to remove cosmic ray events which appear to be hot pixels. These hot pixel maps obtained during EEIS testing were compared to the maps made during module testing to verify that the end-to-end data flow was correct.

Standley, Shaun P.↗
NASA NTRS2012

Downsampling Photodetector Array with Windowing

In a photon counting detector array, each pixel in the array produces an electrical pulse when an incident photon on that pixel is detected. Detection and demodulation of an optical communication signal that modulated the intensity of the optical signal requires counting the number of photon arrivals over a given interval. As the size of photon counting photodetector arrays increases, parallel processing of all the pixels exceeds the resources available in current application-specific integrated circuit (ASIC) and gate array (GA) technology; the desire for a high fill factor in avalanche photodiode (APD) detector arrays also precludes this. Through the use of downsampling and windowing portions of the detector array, the processing is distributed between the ASIC and GA. This allows demodulation of the optical communication signal incident on a large photon counting detector array, as well as providing architecture amenable to algorithmic changes. The detector array readout ASIC functions as a parallel-to-serial converter, serializing the photodetector array output for subsequent processing. Additional downsampling functionality for each pixel is added to this ASIC. Due to the large number of pixels in the array, the readout time of the entire photodetector is greater than the time between photon arrivals; therefore, a downsampling pre-processing step is done in order to increase the time allowed for the readout to occur. Each pixel drives a small counter that is incremented at every detected photon arrival or, equivalently, the charge in a storage capacitor is incremented. At the end of a user-configurable counting period (calculated independently from the ASIC), the counters are sampled and cleared. This downsampled photon count information is then sent one counter word at a time to the GA. For a large array, processing even the downsampled pixel counts exceeds the capabilities of the GA. Windowing of the array, whereby several subsets of pixels are designated for processing, is used to further reduce the computational requirements. The grouping of the designated pixel frame as the photon count information is sent one word at a time to the GA, the aggregation of the pixels in a window can be achieved by selecting only the designated pixel counts from the serial stream of photon counts, thereby obviating the need to store the entire frame of pixel count in the gate array. The pixel count se quence from each window can then be processed, forming lower-rate pixel statistics for each window. By having this processing occur in the GA rather than in the ASIC, future changes to the processing algorithm can be readily implemented. The high-bandwidth requirements of a photon counting array combined with the properties of the optical modulation being detected by the array present a unique problem that has not been addressed by current CCD or CMOS sensor array solutions.

Patawaran, Ferze D.↗
DOE OSTI2017

Acquisition of STEM Images by Adaptive Compressive Sensing

Compressive Sensing (CS) allows a signal to be sparsely measured first and accurately recovered later in software [1]. In scanning transmission electron microscopy (STEM), it is possible to compress an image spatially by reducing the number of measured pixels, which decreases electron dose and increases sensing speed [2,3,4]. The two requirements for CS to work are: (1) sparsity of basis coefficients and (2) incoherence of the sensing system and the representation system. However, when pixels are missing from the image, it is difficult to have an incoherent sensing matrix. Nevertheless, dictionary learning techniques such as Beta-Process Factor Analysis (BPFA) [5] are able to simultaneously discover a basis and the sparse coefficients in the case of missing pixels. On top of CS, we would like to apply active learning [6,7] to further reduce the proportion of pixels being measured, while maintaining image reconstruction quality. Suppose we initially sample 10% of random pixels. We wish to select the next 1% of pixels that are most useful in recovering the image. Now, we have 11% of pixels, and we want to decide the next 1% of “most informative” pixels. Active learning methods are online and sequential in nature. Our goal is to adaptively discover the best sensing mask during acquisition using feedback about the structures in the image. In the end, we hope to recover a high quality reconstruction with a dose reduction relative to the non-adaptive (random) sensing scheme. In doing this, we try three metrics applied to the partial reconstructions for selecting the new set of pixels: (1) variance, (2) Kullback-Leibler (KL) divergence using a Radial Basis Function (RBF) kernel, and (3) entropy. Figs. 1 and 2 display the comparison of Peak Signal-to-Noise (PSNR) using these three different active learning methods at different percentages of sampled pixels. At 20% level, all the three active learning methods underperform the original CS without active learning. However, they all beat the original CS as more of the “most informative” pixels are sampled. One can also argue that CS equipped with active learning requires less sampled pixels to achieve the same value of PSNR than CS with pixels randomly sampled, since all the three PSNR curves with active learning grow at a faster pace than that without active learning. For this particular STEM image, by observing the reconstructed images and the sensing masks, we find that while the method based on RBF kernel acquires samples more uniformly, the one on entropy samples more areas of significant change, thus less uniformly. The KL-divergence method performs the best in terms of reconstruction error (PSNR) for this example [8].

Xie, Weiyi↗
NASA NTRS2004

Video Image Tracking Engine

A method and system for processing an image including capturing an image and storing the image as image pixel data. Each image pixel datum is stored in a respective memory location having a corresponding address. Threshold pixel data is selected from the image pixel data and linear spot segments are identified from the threshold pixel data selected.. Ihe positions of only a first pixel and a last pixel for each linear segment are saved. Movement of one or more objects are tracked by comparing the positions of fust and last pixels of a linear segment present in the captured image with respective first and last pixel positions in subsequent captured images. Alternatively, additional data for each linear data segment is saved such as sum of pixels and the weighted sum of pixels i.e., each threshold pixel value is multiplied by that pixel's x-location).

Howard, Richard T.↗
NASA NTRSDate not supplied

X-Ray Missions Based on LTD Technologies

Microcalorimeter arrays used as imaging spectrometers will enable ground-breaking science in X-ray astrophysics. Their development has been one of the driving applications of the low temperature detector community since the early 1980s. While their application in space remains in relative infancy, we are on the verge of an era in which microcalorimeter spectrometers are likely to dominate instrumentation for high-energy astrophysics missions. NASA sounding rocket experiments such as the X-ray Quantum Calorimeter (XQC) and Micro-X have now paved the way for orbital observatories. JAXA’s Astro-H gave us a glimpse of this fantastic new capability before its unfortunate demise. The launch of XRISM later this year should finally demonstrate the sort of large suite of observations that X-ray microcalorimeter arrays will make major contributions to. In the mid-2030s, we will see the launch of ESA’s flagship Athena mission, that will further advance the imaging and spectroscopic capabilities for the high-energy X-ray community enormously. Numerous other potential new mission concepts using X-ray microcalorimeters have been studied in detail such as NASA’s Lynx and the Light Element Mapper (LEM), JAXA’s Super-DIOS, and China’s Hot Universe Baryon Surveyor (HUBS). In the first half of this presentation, I will review the development and desired requirements of the X-ray microcalorimeter missions and concepts, and describe the current progress and status missions being implemented and proposed. In the second half of this presentation, I will focus on The Line Emission Mapper (LEM) which is a probe-class mission concept under study by NASA that is designed to provide unprecedented insight into the physics of galaxy formation, including stellar and black-hole feedback and flows of baryonic matter into and out of galaxies. LEM incorporates a light-weight X-ray optic with 10” angular resolution and a large-format microcalorimeter array with a 15” pixel pitch over a 29’ field of view (FOV). The main microcalorimeter array and readout design takes advantage of mature technology that has been developed for Athena X-IFU. The central 7’ region of the array consists of ~ 800 transition-edge sensors (TESs), optimized for a bandpass of 0.3-2 keV and with an energy resolution of 1.2 eV at 1 keV. The rest of the FOV is covered by 4-pixel TES hydras with 2.5 eV energy resolution at 1 keV. Hydras are position-sensitive (‘thermally multiplexed’) detectors that enable extremely large numbers of effective pixels in an array, without a commensurate increase in the number of wires, bias circuit, and readout components. In total the array will have ~ 4k TESs with ~ 14k imaging elements on a 290 micron-pitch. The array will be read out with state-of-the-art time-division multiplexing (TDM), implement with 59 pixels (and 1 dark pixel) in each of the 69 TDM columns. Here we present a detailed overview of the baseline microcalorimeter detector design and present first results from both single pixel TESs and 4-pixel hydras. Our prototype LEM pixels utilize 15um x 50um Mo/Au TESs with a transition temperature of around 58 mK. These pixels incorporate high-fill-factor Au absorbers of thickness 0.54um. We have demonstrated an energy resolution of 0.90+/-0.02 eV and 1.90 +/-0.02 eV at 1.5 keV for single pixel and 4-pixel hydras, respectively, meeting the performance requirements of LEM. We show how the position discrimination in the hydra pixels can be achieved, down to energies of a few 100 eV, using the measured pulse rise-times. Finally, we will show that the properties of the LEM pixels are well optimized for the proposed TDM read out architecture.

Simon Richard Bandler↗
DOE OSTI2018

Fast Fiber-Coupled Imaging Devices

HyperV Technologies Corp. has successfully designed, built and experimentally demonstrated a full scale 1024 pixel 100 MegaFrames/s fiber coupled camera with 12 or 14 bits, and record lengths of 32K frames, exceeding our original performance objectives. This high-pixel-count, fiber optically-coupled, imaging diagnostic can be used for investigating fast, bright plasma events. In Phase 1 of this effort, a 100 pixel fiber-coupled fast streak camera for imaging plasma jet profiles was constructed and successfully demonstrated. The resulting response from outside plasma physics researchers emphasized development of increased pixel performance as a higher priority over increasing pixel count. In this Phase 2 effort, HyperV therefore focused on increasing the sample rate and bit-depth of the photodiode pixel designed in Phase 1, while still maintaining a long record length and holding the cost per channel to levels which allowed up to 1024 pixels to be constructed. Cost per channel was 53.31 dollars, very close to our original target of $50 per channel. The system consists of an imaging "camera head" coupled to a photodiode bank with an array of optical fibers. The output of these fast photodiodes is then digitized at 100 Megaframes per second and stored in record lengths of 32,768 samples with bit depths of 12 to 14 bits per pixel. Longer record lengths are possible with additional memory. A prototype imaging system with up to 1024 pixels was designed and constructed and used to successfully take movies of very fast moving plasma jets as a demonstration of the camera performance capabilities. Some faulty electrical components on the 64 circuit boards resulted in only 1008 functional channels out of 1024 on this first generation prototype system. We experimentally observed backlit high speed fan blades in initial camera testing and then followed that with full movies and streak images of free flowing high speed plasma jets (at 30-50 km/s). Jet structure and jet collisions onto metal pillars in the path of the plasma jets were recorded in a single shot. This new fast imaging system is an attractive alternative to conventional fast framing cameras for applications and experiments where imaging events using existing techniques are inefficient or impossible. The development of HyperV's new diagnostic was split into two tracks: a next generation camera track, in which HyperV built, tested, and demonstrated a prototype 1024 channel camera at its own facility, and a second plasma community beta test track, where selected plasma physics programs received small systems of a few test pixels to evaluate the expected performance of a full scale camera on their experiments. These evaluations were performed as part of an unfunded collaboration with researchers at Los Alamos National Laboratory and the University of California at Davis. Results from the prototype 1024-pixel camera are discussed, as well as results from the collaborations with test pixel system deployment sites.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2007

Increasing Linear Dynamic Range of a CMOS Image Sensor

A generic design and a corresponding operating sequence have been developed for increasing the linear-response dynamic range of a complementary metal oxide/semiconductor (CMOS) image sensor. The design provides for linear calibrated dual-gain pixels that operate at high gain at a low signal level and at low gain at a signal level above a preset threshold. Unlike most prior designs for increasing dynamic range of an image sensor, this design does not entail any increase in noise (including fixed-pattern noise), decrease in responsivity or linearity, or degradation of photometric calibration. The figure is a simplified schematic diagram showing the circuit of one pixel and pertinent parts of its column readout circuitry. The conventional part of the pixel circuit includes a photodiode having a small capacitance, CD. The unconventional part includes an additional larger capacitance, CL, that can be connected to the photodiode via a transfer gate controlled in part by a latch. In the high-gain mode, the signal labeled TSR in the figure is held low through the latch, which also helps to adapt the gain on a pixel-by-pixel basis. Light must be coupled to the pixel through a microlens or by back illumination in order to obtain a high effective fill factor; this is necessary to ensure high quantum efficiency, a loss of which would minimize the efficacy of the dynamic- range-enhancement scheme. Once the level of illumination of the pixel exceeds the threshold, TSR is turned on, causing the transfer gate to conduct, thereby adding CL to the pixel capacitance. The added capacitance reduces the conversion gain, and increases the pixel electron-handling capacity, thereby providing an extension of the dynamic range. By use of an array of comparators also at the bottom of the column, photocharge voltages on sampling capacitors in each column are compared with a reference voltage to determine whether it is necessary to switch from the high-gain to the low-gain mode. Depending upon the built-in offset in each pixel and in each comparator, the point at which the gain change occurs will be different, adding gain-dependent fixed pattern noise in each pixel. The offset, and hence the fixed pattern noise, is eliminated by sampling the pixel readout charge four times by use of four capacitors (instead of two such capacitors as in conventional design) connected to the bottom of the column via electronic switches SHS1, SHR1, SHS2, and SHR2, respectively, corresponding to high and low values of the signals TSR and RST. The samples are combined in an appropriate fashion to cancel offset-induced errors, and provide spurious-free imaging with extended dynamic range.

Pain, Bedabrata↗
NASA NTRS2009

Algorithm for Detecting a Bright Spot in an Image

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.)

Source record↗
NASA NTRS2010

Salience Assignment for Multiple-Instance Data and Its Application to Crop Yield Prediction

An algorithm was developed to generate crop yield predictions from orbital remote sensing observations, by analyzing thousands of pixels per county and the associated historical crop yield data for those counties. The algorithm determines which pixels contain which crop. Since each known yield value is associated with thousands of individual pixels, this is a multiple instance learning problem. Because individual crop growth is related to the resulting yield, this relationship has been leveraged to identify pixels that are individually related to corn, wheat, cotton, and soybean yield. Those that have the strongest relationship to a given crop s yield values are most likely to contain fields with that crop. Remote sensing time series data (a new observation every 8 days) was examined for each pixel, which contains information for that pixel s growth curve, peak greenness, and other relevant features. An alternating-projection (AP) technique was used to first estimate the "salience" of each pixel, with respect to the given target (crop yield), and then those estimates were used to build a regression model that relates input data (remote sensing observations) to the target. This is achieved by constructing an exemplar for each crop in each county that is a weighted average of all the pixels within the county; the pixels are weighted according to the salience values. The new regression model estimate then informs the next estimate of the salience values. By iterating between these two steps, the algorithm converges to a stable estimate of both the salience of each pixel and the regression model. The salience values indicate which pixels are most relevant to each crop under consideration.

Wagstaff, Kiri L.↗
NASA NTRS2002

Fractional Snowcover Estimates from Earth Observing System (EOS) Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS)

The Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Earth Observing System (EOS) Terra and Aqua missions has shown considerable capability for mapping snowcover. The typical approach that has used, along with other criteria, the Normalized Snow Difference Index (NDSI) that takes the difference between 500 meter observations at 1.64 micrometers (MODIS band 6) and 0.555 micrometers (MODIS band 4) over the sum of these observations to determine whether MODIS pixels are snowcovered or not in mapping the extent of snowcover. For many hydrological and climate studies using remote sensing of snowcover, it is desirable to assess if the MODIS snowcover observations could not be enhanced by providing the fraction of snowcover in each MODIS observation (pixel). Pursuant to this objective studies have been conducted to assess whether there is sufficient "signal%o in the NDSI parameter to provide useful estimates of fractional snowcover in each MODIS 500 meter pixel. To accomplish this objective high spatial resolution (30 meter) Landsat snowcover observations were used and co-registered with MODIS 500 meter pixels. The NDSI approach was used to assess whether a Landsat pixel was or was not snowcovered. Then the number of snowcovered Landsat pixels within a MODIS pixel was used to determine the fraction of snowcover within each MODIS pixel. The e results were then used to develop statistical relationships between the NDSI value for each 500 meter MODIS pixel and the fraction of snowcover in the MODIS pixel. Such studies were conducted for three widely different areas covered by Landsat scenes in Alaska, Russia, and the Quebec Province in Canada. The statistical relationships indicate that a 10 percent accuracy can be attained. The variability in the statistical relationship for the three areas was found to be remarkably similar (-0.02 for mean error and less than 0.01 for mean absolute error and standard deviation). Independent tests of the relationships were accomplished by taking the relationship of fractional snow-cover to NDSI from one area (e.g., Alaska) and testing it on the other two areas (e.g. Russia and Quebec). Again the results showed that fractional snow-cover can be estimated to 10 percent. The results have been shown to have advantages over other published fractional snowcover algorithms applied to MODIS data. Most recently the fractional snow-cover algorithm has been applied using 500-meter observations over the state of Colorado for a period spanning 25 days. The results exhibit good behavior in mapping the spatial and temporal variability in snowcover over that 25-day period. Overall these studies indicate that robust estimates of fractional snow-cover can be attained using the NDSI parameter over areas extending in size from watersheds relatively large compared to MODIS pixels to global land cover. Other refinements to this approach as well as different approaches are being examined for mapping fractional snow-cover using MODIS observations.

Salomonson, Vincent V.↗
NASA NTRS2012

Resolution Enhancement of MODIS-Derived Water Indices for Studying Persistent Flooding

Monitoring coastal marshes for persistent flooding and salinity stress is a high priority issue in Louisiana. Remote sensing can identify environmental variables that can be indicators of marsh habitat conditions, and offer timely and relatively accurate information for aiding wetland vegetation management. Monitoring activity accuracy is often limited by mixed pixels which occur when areas represented by the pixel encompasses more than one cover type. Mixtures of marsh grasses and open water in 250m Moderate Resolution Imaging Spectroradiometer (MODIS) data can impede flood area estimation. Flood mapping of such mixtures requires finer spatial resolution data to better represent the cover type composition within 250m MODIS pixel. Fusion of MODIS and Landsat can improve both spectral and temporal resolution of time series products to resolve rapid changes from forcing mechanisms like hurricane winds and storm surge. For this study, using a method for estimating sub-pixel values from a MODIS time series of a Normalized Difference Water Index (NDWI), using temporal weighting, was implemented to map persistent flooding in Louisiana coastal marshes. Ordinarily NDWI computed from daily 250m MODIS pixels represents a mixture of fragmented marshes and water. Here, sub-pixel NDWI values were derived for MODIS data using Landsat 30-m data. Each MODIS pixel was disaggregated into a mixture of the eight cover types according to the classified image pixels falling inside the MODIS pixel. The Landsat pixel means for each cover type inside a MODIS pixel were computed for the Landsat data preceding the MODIS image in time and for the Landsat data succeeding the MODIS image. The Landsat data were then weighted exponentially according to closeness in date to the MODIS data. The reconstructed MODIS data were produced by summing the product of fractional cover type with estimated NDWI values within each cover type. A new daily time series was produced using both the reconstructed 250-m MODIS, with enhanced features, and the approximated daily 30-m high-resolution image based on Landsat data. The algorithm was developed and tested over the Calcasieu-Sabine Basin, which was heavily inundated by storm surge from Hurricane Ike to study the extent and duration of flooding following the storm. Time series for 2000-2009, covering flooding events by Hurricane Rita in 2005 and Hurricane Ike in 2008, were derived. High resolution images were formed for all days in 2008 between the first cloud free Landsat scene and the last cloud-free Landsat scene. To refine and validate flooding maps, each time series was compared to Louisiana Coastwide Reference Monitoring System (CRMS) station water levels adjusted to marsh to optimize thresholds for MODIS-derived time series of NDWI. Seasonal fluctuations were adjusted by subtracting ten year average NDWI for marshes, excluding the hurricane events. Results from different NDWI indices and a combination of indices were compared. Flooding persistence that was mapped with higher-resolution data showed some improvement over the original MODIS time series estimates. The advantage of this novel technique is that improved mapping of extent and duration of inundation can be provided.

Underwood, L. W.↗
DOE OSTI2019

Texture analysis of small renal cell carcinomas at MDCT for predicting relevant histologic and protein biomarkers

Purpose: To assess CT texture features of small renal cell carcinomas (≤ 4cm) for association with key pathologic features including protein biomarkers. Methods: Quantitative CT texture analysis (CTTA) of small renal cancers (≤ 4cm) was performed on non-contrast and portal venous phase abdominal MDCT scans with an ROI drawn at the largest cross-sectional diameter of the tumor using commercially available software. Texture parameters including mean pixel attenuation, the standard deviation (SD) of the pixel distribution histogram, entropy, the mean of positive pixels, the skewness (i.e., asymmetry) of the pixel histogram, kurtosis (i.e., peakness) of the pixel histogram, and the percentage of positive pixels were correlated with pathologic data from surgical resection, including histology and nuclear grade, as well as microarray analysis in a subset (n = 40) including Ki67 index, CRP, and neovascularization (CD105/CD31). Results: Portal venous phase images were available in 249 patients (105 women, 144 men; mean age, 56.7 years) with tumors ≤ 4cm (mean, median, range, ± SD; 2.66, 2.60, 0.3–4.0 ± 0.85 cm). CT texture features of standard deviation, mean of the positive pixels, and entropy of the pixel histogram were significantly associated with histologic cell type (clear vs. non-clear; p < 0.001). Entropy and mean of the positive pixels also showed an association with nuclear grade, although not statistically significant. In the microarray analysis subset, kurtosis of the pixel histogram was associated with CD105/CD31 (p = 0.05). SD also showed some association with CD 105 positivity (p = 0.02) and CAIX expression (p = 0.01). Non-contrast CT images were available in 174 patients (72 women, 102 men; mean age, 57.5 years). Although the association with histology was not as strong as on the portal venous phase, in the subset of patients with microarray data, SD was found to correlate with CRP (p = 0.08), kurtosis with CRP (p = 0.004), CD105/CD31 (p = 0.002), and with Ki 67 index (p < 0.001). Conclusion: CT texture features were significantly associated with important histopathologic features in small renal cancers. These non-invasive measures can be performed retrospectively and may provide useful information when determining follow-up and treatment of small renal cancers.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Indium-bump-free antimonide superlattice membrane detectors on silicon substrates

We present an approach to realize antimonide superlattices on silicon substrates without using conventional Indium-bump hybridization. In this approach, PIN superlattices are grown on top of a 60 nm Al0.6Ga0.4Sb sacrificial layer on a GaSb host substrate. Following the growth, the individual pixels are transferred using our epitaxial-lift off technique, which consists of a wet-etch to undercut the pixels followed by a dry-stamp process to transfer the pixels to a silicon substrate prepared with a gold layer. Structural and optical characterization of the transferred pixels was done using an optical microscope, scanning electron microscopy, and photoluminescence. The interface between the transferred pixels and the new substrate was abrupt, and no significant degradation in the optical quality was observed. An Indium-bump-free membrane detector was then fabricated using this approach. Spectral response measurements provided a 100% cut-off wavelength of 4.3 μm at 77 K. The performance of the membrane detector was compared to a control detector on the as-grown substrate. The membrane detector was limited by surface leakage current. The proposed approach could pave the way for wafer-level integration of photonic detectors on silicon substrates, which could dramatically reduce the cost of these detectors.

Zamiri, M. (ORCID:000000020042735X)↗
NASA NTRS1982

The digital step edge

The facet model was used to accomplish step edge detection. The essence of the facet model is that any analysis made on the basis of the pixel values in some neighborhood has its final authoritative interpretation relative to the underlying grey tone intensity surface of which the neighborhood pixel values are observed noisy samples. Pixels which are part of regions have simple grey tone intensity surfaces over their areas. Pixels which have an edge in them have complex grey tone intensity surfaces over their areas. Specially, an edge moves through a pixel only if there is some point in the pixel's area having a zero crossing of the second directional derivative taken in the direction of a non-zero gradient at the pixel's center. To determine whether or not a pixel should be marked as a step edge pixel, its underlying grey tone intensity surface was estimated on the basis of the pixels in its neighborhood.

Haralick, R. M.↗