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All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Large format, high resolution images sensors

The performance requirements for scientific-quality CCDs are discussed, focusing on the design of two devices, and the progress toward achieving the desired performance is discussed. These devices are intended for rear-illuminated applications and have 512 x 512 and 2048 x 2048 pixel formats. The thinned 10 to 20 micron thick Si membrane is fully supported by a unique glass ceramic substrate. Quantum efficiencies of greater than 70 percent at 700 nm and greater than 40 percent at wavelengths less than 400 nm have been measured on a test device. Dark currents as low as 6 pA/sq cm also have been measured recently.

Blouke, Morley M.↗
NASA NTRS1993

Quality metrics for sensor images

Methods are needed for evaluating the quality of augmented visual displays (AVID). Computational quality metrics will help summarize, interpolate, and extrapolate the results of human performance tests with displays. The FLM Vision group at NASA Ames has been developing computational models of visual processing and using them to develop computational metrics for similar problems. For example, display modeling systems use metrics for comparing proposed displays, halftoning optimizing methods use metrics to evaluate the difference between the halftone and the original, and image compression methods minimize the predicted visibility of compression artifacts. The visual discrimination models take as input two arbitrary images A and B and compute an estimate of the probability that a human observer will report that A is different from B. If A is an image that one desires to display and B is the actual displayed image, such an estimate can be regarded as an image quality metric reflecting how well B approximates A. There are additional complexities associated with the problem of evaluating the quality of radar and IR enhanced displays for AVID tasks. One important problem is the question of whether intruding obstacles are detectable in such displays. Although the discrimination model can handle detection situations by making B the original image A plus the intrusion, this detection model makes the inappropriate assumption that the observer knows where the intrusion will be. Effects of signal uncertainty need to be added to our models. A pilot needs to make decisions rapidly. The models need to predict not just the probability of a correct decision, but the probability of a correct decision by the time the decision needs to be made. That is, the models need to predict latency as well as accuracy. Luce and Green have generated models for auditory detection latencies. Similar models are needed for visual detection. Most image quality models are designed for static imagery. Watson has been developing a general spatial-temporal vision model to optimize video compression techniques. These models need to be adapted and calibrated for AVID applications.

Ahumada, AL↗
NASA NTRS2007

Diurnal Lightning Distributions as Observed by the Optical Transient Detector (OTD) and the Lightning Imaging Sensor (LIS)

Data obtained from the OTD (April 1995 to March 2000) and LIS (December 1997 to December 2005) satellites (70 and 35 degree inclination low earth orbits, respectively) are used to statistically determine the number of flashes in the diurnal cycle both as a function of local and universal time. Also included are global flash density maps. The data is further subdivided by season, continental versus oceanic, night time versus day time, northern versus southern hemisphere, and other regions of interest such as the Brazilian rain forest and the Congo. The data includes corrections for detection efficiency and instrument view time. The data was compared with the "Carnegie Curve" and the diurnal global thunderstorm contributions from thunderday statistics fiom different continents as derived by Whipple and Scrase, 1936. Initial results indicate that the northern hemisphere fall (September to November) has a larger amplitude than the northern hemisphere spring (March to May). This may be due to the differences in the contribution of the Brazilian rain forest during these periods (the main lightning peak starts in September and ends in February with a secondary peak in April). For some areas of the world, the peak of the diurnal curve appears to be shifted to later in the evening.

Blakeslee, R. J.↗
NASA NTRS2007

Diurnal Lightning Distributions as Observed by the Optical Transient Detector (OTD) and the Lightning Imaging Sensor (LIS)

Data obtained from the OTD (April 1995 to March 2000) and LIS (December 1997 to December 2005) satellites (70 and 35 degree inclination low earth orbits, respectively) are used to statistically determine the number of flashes in the diurnal cycle both as a function of local and universal time. Also included are global flash density maps. The data are further subdivided by season, continental versus oceanic, night time versus day time, northern versus southern hemisphere, and other regions of interest. The data include corrections for detection efficiency and instrument view time. The data are compared with both the "Carnegie Curve" and the diurnal global thunderstorm contributions from thunderday statistics from different continents, and are found to agree closely in phase and amplitude with the global thunderday statistics. The analysis also indicates that the southern hemisphere spring (September to November) has larger amplitude than the southern hemisphere fall (March to May). This may be due to differences in the contribution from the Brazilian rain forest during these periods. In general, as highlighted by a difference analysis, more lightning is observed in local springtime than the fall for continental locations, while oceanic regions display an opposite effect. For some areas of the world, the peak of diurnal curve appears to be shifted to later in the evening.

Bailey, Jeff C.↗
NASA NTRS2008

Generic Helicopter-Based Testbed for Surface Terrain Imaging Sensors

To be certain that a candidate sensor system will perform as expected during missions, we have developed a field test system and have executed test flights with a helicopter-mounted sensor platform over desert terrains, which simulate Lunar features. A key advantage to this approach is that different sensors can be tested and characterized in an environment relevant to the flight needs prior to flight. Testing the various sensors required the development of a field test system, including an instrument to validate the truth of the sensor system under test. The field test system was designed to be flexible enough to cover the test needs of many sensors (lidar, radar, cameras) that require an aerial test platform, including helicopters, airplanes, unmanned aerial vehicles (UAV), or balloons. To validate the performance of the sensor under test, the dynamics of the test platform must be known with sufficient accuracy to provide accurate models for input into algorithm development. The test system provides support equipment to measure the dynamics of the field test sensor platform, and allow computation of the truth position, velocity, attitude, and time.

Alexander, James↗
NASA NTRS2012

Star sensing for an earth imaging sensor

A star sensor includes (a) a scan mirror for scanning at least one star; (b) a detector array, coupled to the scan mirror, for detecting the one star; and (c) a processor, coupled to the detector array. The processor includes a first filter configured to reduce noise spikes in the detected one star, and provide a detection mask of filtered data. Also included is a second filter configured to reduce non-contiguous samples in the detection mask. A centroid calculator is included to determine a location of the one star, after the first and second filtering. The first filter includes a median filter, followed by an averaging filter, both configured to filter the one star in an along-scan direction of the scan mirror. The first filter includes another median filter, which is configured to filter the detected one star in the cross-scan direction of the scan mirror. An adder is included to subtract (a) output data from the other median filter from (b) output data from the averaging filter and provide filtered star data to the second filter.

Ellis, Kenneth K.↗
NASA NTRS2012

Cross-calibration of Imaging Sensors using Model-based, SI-traceable Predictions of At-sensor Radiance

Many inter-consistency efforts force empirical agreement between sensors viewing a source nearly coincident in time and geometry that ensures consistency between sensors rather than obtain an SI-traceable calibration with documented error budgets. The method described here provides interconsistency via absolute radiometric calibration with defensible error budget avoiding systematic errors through prediction of at-sensor radiance for a site viewed by multiple sensors but not necessarily viewed at coincident times. The method predicts spectral radiance over a given surface site for arbitrary view and illumination angles and for any date dominated by clear-sky conditions. The foundation is a model-based, SI-traceable prediction of at-sensor radiance over selected sites based on physical understanding of the surface and atmosphere. The calibration of the ground site will include spatial, spectral, and sunview geometric effects based on satellite and ground-based data. The result is an interconsistency of hyperspectral and multispectral sensors spanning spatial resolutions from meters to kilometers all relative to the surface site rather than a single sensor. The source-centric philosophy of calibrating the site inherently accounts for footprint size mismatch, spectral band mismatch, and temporal and spatial sampling effects. The method for characterizing the test site allows its use for SI-traceable calibration of any sensor that can view the calibrated test site. Interconsistency is obtained through the traceability and error budget rather than coincident views. Such an approach to inter-consistency provides better understanding of biases between sensors as well producing more accurate results with documented SI-traceability that reduces the need for overlapping data sets.

Thome, Kurtis↗
NASA NTRS2013

3D Radiative Transfer Effects in Multi-Angle/Multi-Spectral Radio-Polarimetric Signals from a Mixture of Clouds and Aerosols Viewed by a Non-Imaging Sensor

When observing a spatially complex mix of aerosols and clouds in a single relatively large field-of-view, nature entangles their signals non-linearly through polarized radiation transport processes that unfold in the 3D position and direction spaces. In contrast, any practical forward model in a retrieval algorithm will use only 1D vector radiative transfer (vRT) in a linear mixing technique. We assess the difference between the observed and predicted signals using synthetic data from a high-fidelity 3D vRT model with clouds generated using a Large Eddy Simulation model and an aerosol climatology. We find that this difference is signal--not noise--for the Aerosol Polarimetry Sensor (APS), an instrument developed by NASA. Moreover, the worst case scenario is also the most interesting case, namely, when the aerosol burden is large, hence hase the most impact on the cloud microphysics and dynamics. Based on our findings, we formulate a mitigation strategy for these unresolved cloud adjacency effects assuming that some spatial information is available about the structure of the clouds at higher resolution from "context" cameras, as was planned for NASA's ill-fated Glory mission that was to carry the APS but failed to reach orbit. Application to POLDER (POLarization and Directionality of Earth Reflectances) data from the period when PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) was in the A-train is briefly discussed.

APS↗