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NASA NTRS2012

NASA IMAGESEER: NASA IMAGEs for Science, Education, Experimentation and Research

A number of web-accessible databases, including medical, military or other image data, offer universities and other users the ability to teach or research new Image Processing techniques on relevant and well-documented data. However, NASA images have traditionally been difficult for researchers to find, are often only available in hard-to-use formats, and do not always provide sufficient context and background for a non-NASA Scientist user to understand their content. The new IMAGESEER (IMAGEs for Science, Education, Experimentation and Research) database seeks to address these issues. Through a graphically-rich web site for browsing and downloading all of the selected datasets, benchmarks, and tutorials, IMAGESEER provides a widely accessible database of NASA-centric, easy to read, image data for teaching or validating new Image Processing algorithms. As such, IMAGESEER fosters collaboration between NASA and research organizations while simultaneously encouraging development of new and enhanced Image Processing algorithms. The first prototype includes a representative sampling of NASA multispectral and hyperspectral images from several Earth Science instruments, along with a few small tutorials. Image processing techniques are currently represented with cloud detection, image registration, and map cover/classification. For each technique, corresponding data are selected from four different geographic regions, i.e., mountains, urban, water coastal, and agriculture areas. Satellite images have been collected from several instruments - Landsat-5 and -7 Thematic Mappers, Earth Observing-1 (EO-1) Advanced Land Imager (ALI) and Hyperion, and the Moderate Resolution Imaging Spectroradiometer (MODIS). After geo-registration, these images are available in simple common formats such as GeoTIFF and raw formats, along with associated benchmark data.

Le Moigne, Jacqueline↗
NASA NTRS2020

An Algorithm for Hyperspectral Remote Sensing of Aerosols: 3. Application to the GEO-TASO Data in KORUS-AQ Field Campaign

This paper describes the third part of a series of investigations to develop algorithms for simultaneous retrieval of aerosol parameters and surface spectral reflectance from the data measured by GEOstationary Trace gas and Aerosol Sensor Optimization (GEO-TASO) instrument. Since the algorithm is designed for future hyperspectral and geostationary satellite sensors, such as Tropospheric Emissions: Monitoring of Pollution (TEMPO), it is applied to GEO-TASO data measured over the same area by different flights as part of the Korea-United Stated Air Quality Study (KORUS-AQ) field campaign in 2016. While GEO-TAOS has a spectral sampling interval of ~0.28 nm in the visible, its data is thinned through a band selection approach with consideration of atmospheric transmittance and different surface types, which yields 20 common spectral bands to be used by the algorithm. The algorithm starts with 4 common principal components (PCs) for surface spectral reflectance extracted from various spectral libraries, and the constraints of surface reflectance parameters and aerosol scattering models respectively from k- means clustering analysis of the Rayleigh-corrected GEO-TASO spectra and AERONET data. The algorithm then proceeds iteratively with an optimal estimation approach to update PCs and retrieve aerosol optical depth (AOD) until the best match between simulated and GEOS-TASO measured spectra is achieved. The spectral AODs are compared between those retrieved from GEO-TASO (x) and 7 AERONETS (y) in 440, 550, 550, 675 nm, respectively. The comparison reveals that iterative updates of surface spectral PCs (and so surface reflectance) yield significant enhancement in AOD retrievals, improving the mean linear fitting equations from y = 0.735x + 0.088 (without update of PCs) to y = 1.055x+ 0.01, and the Pearson correlation coefficient (𝑅𝑅2) from 0.54 to 0.76, respectively. Move case studies are need to further evaluate the algorithm for its application to TEMPO that carries a enhanced version of GEO-TASO instrument.

Tropospheric Emissions: Monitoring of Pollution (T↗
NASA NTRS2004

The formation algorithms and simulation testbed

Formation flying for spacecraft is a rapidly developing field that will enable a new era of space science. For one of its missions, the Terrestrial Planet Finder (TPF) project has selected a formation flying interferometer design to detect earth-like planets orbiting distant stars. In order to advance technology needed for the TPF formation flying interferometer, the TPF project has been developing a distributed real-time testbed to demonstrate end-toend operation of formation flying with TPF-like functionality and precision. This is the Formation Algorithms and Simulation Testbed (FAST) . This FAST was conceived to bring out issues in timing, data fusion, inter-spacecraft communication, inter-spacecraft sensing and system-wide formation robustness. In this paper we describe the FAST and show results from a two-spacecraft formation scenario. The two-spacecraft simulation is the first time that precision end-to-end formation flying operation has been demonstrated in a distributed real-time simulation environment.

Benowitz, Edward↗
NASA NTRS1975

Computer-aided analysis of Landsat-1 MSS data - A comparison of three approaches, including a 'modified clustering' approach

Three approaches for analyzing Landsat-1 data from Ludwig Mountain in the San Juan Mountain range in Colorado are considered. In the 'supervised' approach the analyst selects areas of known spectral cover types and specifies these to the computer as training fields. Statistics are obtained for each cover type category and the data are classified. Such classifications are called 'supervised' because the analyst has defined specific areas of known cover types. The second approach uses a clustering algorithm which divides the entire training area into a number of spectrally distinct classes. Because the analyst need not define particular portions of the data for use but has only to specify the number of spectral classes into which the data is to be divided, this classification is called 'nonsupervised'. A hybrid method which selects training areas of known cover type but then uses the clustering algorithm to refine the data into a number of unimodal spectral classes is called the 'modified-supervised' approach.

Fleming, M. D.↗
NASA NTRS1983

SETI Pulse Detection Algorithm: Analysis of False-alarm Rates

Some earlier work by the Search for Extraterrestrial Intelligence (SETI) Science Working Group (SWG) on the derivation of spectrum analyzer thresholds for a pulse detection algorithm based on an analysis of false alarm rates is extended. The algorithm previously analyzed was intended to detect a finite sequence of i periodically spaced pulses that did not necessarily occupy the entire observation interval. This algorithm would recognize the presence of such a signal only if all i-received pulse powers exceeded a threshold T(i): these thresholds were selected to achieve a desired false alarm rate, independent of i. To simplify the analysis, it was assumed that the pulses were synchronous with the spectrum sample times. This analysis extends the earlier effort to include infinite and/or asynchronous pulse trains. Furthermore, to decrease the possibility of missing an extraterrestrial intelligence signal, the algorithm was modified to detect a pulse train even if some of the received pulse powers fall below the threshold. The analysis employs geometrical arguments that make it conceptually easy to incorporate boundary conditions imposed on the derivation of the false alarm rates. While the exact results can be somewhat complex, simple closed form approximations are derived that produce a negligible loss of accuracy.

Levitt, B. K.↗
NASA NTRS1982

Computational modeling of multispectral remote sensing systems: Background investigations

A computational model of the deterministic and stochastic process of remote sensing has been developed based upon the results of the investigations presented. The model is used in studying concepts for improving worldwide environment and resource monitoring. A review of various atmospheric radiative transfer models is presented as well as details of the selected model. Functional forms for spectral diffuse reflectance with variability introduced are also presented. A cloud detection algorithm and the stochastic nature of remote sensing data with its implications are considered.

Aherron, R. M.↗
NASA NTRS1984

Determination of sea ice parameters with the Nimbus 7 SMMR

A method of determining sea ice parameters using Nimbus 7 polarized multispectral radiance data obtained with the Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) is presented. Observed radiances from selected areas in the Arctic region for the period February 3-7, 1979 were used in computing algorithm coefficients. Polar maps of sea ice concentration, multiyear fraction, and ice temperature are illustrated for this period. The variation of the mean and standard deviation of ice concentration and multiyear ice fraction for a region of perennial ice cover over the first 11 months of SMMR operation is also presented. Comparisons are made between the calculated sea ice parameters and information obtained from previous studies using aircraft, submarine and surface observations. The absolute accuracy of the SMMR parameters remains uncertain.

Cavalieri, D. J.↗
NASA NTRS1991

Sensor placement for on-orbit modal testing

A systematic procedure of placing accelerometers for the on-orbit modal identification of large flexible space structures is addressed. Target modes for modal testing are selected by examining the modal cost of each mode. Assuming that a time-domain modal identification algorithm such as Eigensystem Realization Algorithm is employed to identify the modes from measured time response data, the sensors are placed to ensure the recovery of the target modes. As an application example of the procedure, an accelerometer placement study for the Space Station Freedom On-Orbit Modal Identification Experiment is presented.

Lim, Tae W.↗
NASA NTRS1992

Satellite aerosols retrieval over land surfaces using the structure functions

Remote sensing of aerosol optical thickness from space is difficult over continental surfaces. There are two retrieval algorithms, one based on the use of dark targets and a second based on contrast reduction between selected pixels. Improvements in the contrast reduction method are reported. A procedure is developed for using the satellite image to evaluate whether conditions for applying the structure method are met. The theoretical background is discussed, and the usefulness of the structure functions is demonstrated. The method is applied to NOAA Advanced Very High Resolution Radiometer (AVHRR) imagery where simultaneous ground measurements are used for validation.

Tanre, D.↗
NASA NTRS1999

Work on Planetary Atmospheres and Planetary Atmosphere Probes

A major objective of the grant was to complete the fabrication, test, and evaluation of the atmosphere structure experiment on the Galileo Probe, and to receive, analyze, and interpret data received from the spacecraft. The grantee was competitively selected to be Principal Investigator of Jupiter's atmosphere structure on the Galileo Probe. His primary motivation was to learn as much as possible about Jupiter's atmosphere by means of a successful atmosphere structure experiment, and to support the needs and schedule of the Galileo Project. After a number of launch delays, the Flight instrument was shipped to Kennedy Space Center 2 years after the start of this collaboration, on April 14, 1989, at which time it was determined from System level tests of the ASI on the Probe that the instrument was in good working order and ready for flight. The spacecraft was launched on October 18, 1989. Data analysis of test and calibration data taken over a period of years of instrument testing was continued in preparation for the encounter. The initial instrument checkout in space was performed on October 26, 1989. The data set received by telemetry was thoroughly analyzed, and a report of the findings was transmitted to the Probe Operations Office on Feb. 28, 1990. Key findings reported were that the accelerometer biases had shifted by less than 1 mg through launch and since calibration at Bell Aerospace in 1983; accelerometer scale factors, evaluated by means of calibration currents, fell on lines of variation with temperature established in laboratory calibrations; pressure sensor offsets, correlated as a function of temperature, fell generally within the limits of several years of ground test data; atmospheric and engineering temperature sensor data were internally consistent within a few tenths of a degree; and the instrument electronics performed all expected functions without any observable fault. Altogether, this checkout was highly encouraging of the prospects of instrument performance, although performed greater than 5 years prior to Jupiter encounter. Capability of decoding the science data from the Experiment Data Record to be provided at encounter was developed and exercised using the tape recording of the first Cruise Checkout data. A team effort was organized to program the selection and combination of data words defining pressure, temperature, acceleration, turbulence, and engineering quantities; to apply decalibration algorithms to convert readings from digital numbers to physical quantities; and to organize the data into a suitable printout. A paper on the Galileo Atmosphere Structure Instrument was written and submitted for publication in a special issue of Space Science Reviews. At the Journal editor's request, the grantee reviewed other Probe instrument papers submitted for this special issue. Calibration data were carefully taken for all experiment sensors and accumulated over a period of 10 years. The data were analyzed, fitted with algorithms, and summarized in a calibration report for use in analyzing and interpreting data returned from Jupiter's atmosphere. The sensors included were the primary science pressure, temperature, and acceleration sensors, and the supporting engineering temperature sensors. This report was distributed to experiment coinvestigators and the Probe Project Office.

Seiff, Alvin↗
NASA NTRS2005

Preliminary Design of a Manned Nuclear Electric Propulsion Vehicle Using Genetic Algorithms

Nuclear electric propulsion (NEP) vehicles will be needed for future manned missions to Mars and beyond. Candidate designs must be identified for further detailed design from a large array of possibilities. Genetic algorithms have proven their utility in conceptual design studies by effectively searching a large design space to pinpoint unique optimal designs. This research combined analysis codes for NEP subsystems with a genetic algorithm. The use of penalty functions with scaling ratios was investigated to increase computational efficiency. Also, the selection of design variables for optimization was considered to reduce computation time without losing beneficial design search space. Finally, trend analysis of a reference mission to the asteroids yielded a group of candidate designs for further analysis.

Irwin, Ryan W.↗
NASA NTRS2009

Improving Regional Forecast by Assimilating Atmospheric InfraRed Sounder (AIRS) Profiles into WRF Model

In data sparse regions, remotely-sensed observations can be used to improve analyses and produce improved forecasts. One such source comes from the Atmospheric InfraRed Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced space-based atmospheric sounding systems. The purpose of this paper is to describe a procedure to optimally assimilate high resolution AIRS profile data into a regional configuration of the Advanced Research WRF (ARW) version 2.2 using WRF-Var. The paper focuses on development of background error covariances for the regional domain and background type, and an optimal methodology for ingesting AIRS temperature and moisture profiles as separate overland and overwater retrievals with different error characteristics. The AIRS thermodynamic profiles are derived from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm and contain information about the quality of each temperature layer. The quality indicators were used to select the highest quality temperature and moisture data for each profile location and pressure level. The analyses were then used to conduct a month-long series of regional forecasts over the continental U.S. The long-term impacts of AIRS profiles on forecast were assessed against verifying NAM analyses and stage IV precipitation data.

Chou, Shih-Hung↗
NASA NTRS2011

Compact Polarimetry in a Low Frequency Spaceborne Context

Compact polarimetry has been shown to be an interesting alternative mode to full polarimetry when global coverage and revisit time are key issues. It consists on transmitting a single polarization, while receiving on two. Several critical points have been identified, one being the Faraday rotation (FR) correction and the other the calibration. When a low frequency electromagnetic wave travels through the ionosphere, it undergoes a rotation of the polarization plane about the radar line of sight for a linearly polarized wave, and a simple phase shift for a circularly polarized wave. In a low frequency radar, the only possible choice of the transmit polarization is the circular one, in order to guaranty that the scattering element on the ground is illuminated with a constant polarization independently of the ionosphere state. This will allow meaningful time series analysis, interferometry as long as the Faraday rotation effect is corrected for the return path. In full-polarimetric (FP) mode, two techniques allow to estimate the FR: Freeman method using linearly polarized data, and Bickel and Bates theory based on the transformation of the measured scattering matrix to a circular basis. In CP mode, an alternate procedure is presented which relies on the bare surface scattering properties. These bare surfaces are selected by the conformity coefficient, invariant with FR. This coefficient is compared to other published classifications to show its potential in distinguishing three different scattering types: surface, doublebounce and volume. The performances of the bare surfaces selection and FR estimation are evaluated on PALSAR and airborne data. Once the bare surfaces are selected and Faraday angle estimated over them, the correction can be applied over the whole scene. The algorithm is compared with both FP techniques. In the last part of the paper, the calibration of a CP system from the point of view of classical matrix transformation methods in polarimetry is proposed.

radar polarization diversity↗
NASA NTRS2017

MapX An In Situ, Full-frame X-Ray Spectroscopic Imager for Planetary Science and Astrobiology

Microbial life exploits micron-scale disequilibria at boundaries where valence, chemical potential, pH, Eh, etc. vary on a length scale commensurate with the organisms - 10's to 100's of microns. The detection of accumulations of the biogenic elements C,N,O,P,S at appropriate concentrations on or in a mineral/ice substrate would constitute permissive evidence of extant life, but context is also required. Does the putative biosignature exist under habitable conditions? Under what conditions of P, T, and chemical potential was the host mineralogy formed? MapX is an in situ robotic spacecraft instrument that images the biogenic elements C, N, O, P, S, as well as the cations of the rock-forming minerals (Na, Mg, Al, Si, K, Ca, Ti, Cr, Mn, Fe) and important anions such as Cl, Fl. MapX provides element maps with less than or equal to100 microns resolution over a 2.5 cm X 2.5 cm area, as well as quantitative XRF spectra from ground- or instrument-selected Regions of Interest (ROI). XRF spectra are converted to mineralogies using ground- or instrument-based algorithms. Either X-ray tube or radioisotope sources such as 244Cm (Alpha-particle and gamma- ray fluorescence) can be used. Fluoresced sample Xrays are imaged onto an X-ray sensitive CCD through an X-ray MicroPore Optic (MPO). The MapX design as well as baseline performance requirements for a MapX instrument intended for life detection / identification of habitable environments will be presented.

Astrobiology↗
NASA NTRS2015

Supercomputing in the Age of Discovering Superearths, Earths and Exoplanet Systems

NASA's Kepler Mission was launched in March 2009 as NASA's first mission capable of finding Earth-size planets orbiting in the habitable zone of Sun-like stars, that range of distances for which liquid water would pool on the surface of a rocky planet. Kepler has discovered over 1000 planets and over 4600 candidates, many of them as small as the Earth. Today, Kepler's amazing success seems to be a fait accompli to those unfamiliar with her history. But twenty years ago, there were no planets known outside our solar system, and few people believed it was possible to detect tiny Earth-size planets orbiting other stars. Motivating NASA to select Kepler for launch required a confluence of the right detector technology, advances in signal processing and algorithms, and the power of supercomputing.

exoplanet↗
NASA NTRS2017

Chasing Exoplanets

NASA's Kepler Mission was launched in March 2009 as NASA's first mission capable of finding Earth-size planets orbiting in the habitable zone of Sun-like stars, that range of distances for which liquid water would pool on the surface of a rocky planet. Kepler has discovered over 1000 planets and over 4600 candidates, many of them as small as the Earth. Today, Kepler's amazing success seems to be a fait accompli to those unfamiliar with her history. But twenty years ago, there were no planets known outside our solar system, and few people believed it was possible to detect tiny Earth-size planets orbiting other stars. Motivating NASA to select Kepler for launch required a confluence of the right detector technology, advances in signal processing and algorithms, and the power of supercomputing.

Kepler Mission↗
NASA NTRS2017

H31G-1596: DeepSAT's CloudCNN: A Deep Neural Network for Rapid Cloud Detection from Geostationary Satellites

Cloud and cloud shadow detection has important applications in weather and climate studies. It is even more crucial when we introduce geostationary satellites into the field of terrestrial remote sensing. With the challenges associated with data acquired in very high frequency (10-15 mins per scan), the ability to derive an accurate cloud shadow mask from geostationary satellite data is critical. The key to the success for most of the existing algorithms depends on spatially and temporally varying thresholds,which better capture local atmospheric and surface effects.However, the selection of proper threshold is difficult and may lead to erroneous results. In this work, we propose a deep neural network based approach called CloudCNN to classify cloudshadow from Himawari-8 AHI and GOES-16 ABI multispectral data. DeepSAT's CloudCNN consists of an encoderdecoder based architecture for binary-class pixel wise segmentation. We train CloudCNN on multi-GPU Nvidia Devbox cluster, and deploy the prediction pipeline on NASA Earth Exchange (NEX) Pleiades supercomputer. We achieved an overall accuracy of 93.29% on test samples. Since, the predictions take only a few seconds to segment a full multispectral GOES-16 or Himawari-8 Full Disk image, the developed framework can be used for real-time cloud detection, cyclone detection, or extreme weather event predictions.

GOES-1↗
NASA NTRS2017

Prospecting for Habitable Worlds

NASAs Kepler Mission was launched in March 2009 as NASAs first mission capable of finding Earth-size planets orbiting in the habitable zone of Sun-like stars, that range of distances for which liquid water would pool on the surface of a rocky planet. Kepler has discovered over 2200 planets and over 2200 candidate planets, many of them as small as the Earth. Forty nine of these are less than twice the size of Earth and orbit in the habitable zone of their stars, all of which are cooler and significantly smaller than the Sun.Today, Keplers amazing success seems to be a fait accompli to those unfamiliar with her history. But twenty years ago, there were no planets known outside our solar system, and few people believed it was possible to detect tiny Earth-size planets orbiting other stars. Indeed, demonstrating that the science was feasible took four proposals to NASAs Discovery Program and extensive research and laboratory demonstrations. Motivating NASA to select Kepler for launch required a confluence of the right detector technology, advances in signal processing and algorithms, and the power of supercomputing. On August 23 2015 we reported the discovery of Kepler-452b, the first small, possibly rocky planet in the habitable zone of a G2 star very similar to our own. Kepler-452b orbits its star once every 385 days in an orbit just 5 larger than that of Earth. This discovery represents an important step towards finding and characterizing small habitable worlds orbiting Sun-like stars.

Jenkins, Jon M.↗