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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

An evaluation of techniques for the extraction of mineral absorption features from high spectral resolution remote sensing data

Airborne Visible/Infrared Imaging Spectrometer data covering the wavelength range between 2000 and 2400 nm are examined for their ability to display the diagnostic mineral absorption features of certain alteration minerals, employing various data processing techniques. The techniques may be separated into two broad categories: scene based techniques that use parameters derived from the data themselves, and correction techniques utilizing external information such as solar/atmospheric models. Results indicate that the data corrected utilizing the LOWTRAN 7 atmospheric transfer code constrained with local weather station data are the most effective at showing the diagnostic absorption features of the regions of known mineralogy and introduce the least number of artifacts into the data.

Rast, Michael↗
NASA NTRS1993

From minerals to rocks: Toward modeling lithologies with remote sensing

High spectral resolution imaging spectroscopy will play an important role in future planetary missions. Sophisticated approaches will be needed to unravel subtle, super-imposed spectral features typically of natural systems, and to maximize the science return of these instruments. Carefully controlled laboratory investigations using homogeneous mineral separates have demonstrated that variations due to solid solution, changes in modal abundances, and the effects of particle size are well understood from a physical basis. In many cases, these variations can be modeled quantitatively using photometric models, mixing approaches, and deconvolution procedures. However, relative to the spectra of individual mineral components, reflectance spectra of rocks and natural surfaces exhibit a reduced spectral contrast. In addition, soils or regolith, which are likely to dominate any natural planetary surface, exhibit spectral properties that have some similarities to the parent materials, but due to weathering and alteration, differences remain that cannot yet be fully recreated in the laboratory or through mixture modeling. A significant challenge is therefore to integrate modeling approaches to derive both lithologic determinations and include the effects of alteration. We are currently conducting laboratory investigations in lithologic modeling to expand upon the basic results of previous analyses with our initial goal to more closely match physical state of natural systems. The effects of alteration are to be considered separately.

Mustard, John F.↗
NASA NTRS2003

Demonstration of extreme wavefront sensing performance on the TPF high contrast imaging testbed

The Terrestrial Platet Finder high contrast imaging testbed facilitates the investigation into the diversity of engineering challenges presented by the goal of direct exo-planet detection, for instance, HCIT offers a high-density deformable mirror to control the optical wavefront errors, a configurable coronagraph to control the diffracted light, and translatable cameras for measuring the focal and pupil planes before and after the coronagraph. In this paper, we summarize the measurements that are obtained about the occulter focal plane.

Trauger, John T.↗
NASA NTRS2018

Far-Infrared Remote-Sensing Enabled by Room-Temperature Thermopile Imagers

This work presents a large-format thermopile focal plane module based on kilo-pixel arrays able to image from UV to far-infrared scenes. The spectral selectivity is achieved by means of butcher block filters integrated into sub-assembly modules as part of the optical telescope of the instrument.

Bux, Sabah↗
NASA NTRS2019

Updated MISR Over-Water Research Aerosol Retrieval Algorithm - Part 2: A Multi-Angle Aerosol Retrieval Algorithm for Shallow, Turbid, Oligotrophic, and Eutrophic Waters

Coastal waters serve as transport pathways to the ocean for all agricultural and other runoff from terrestrial sources, and many are the sites for upwelling of nutrient rich, deep water; they are also some of the most biologically productive on Earth. Estimating the impact coastal waters have on the global carbon budget requires relating satellite-based remote-sensing retrievals of biological productivity (e.g., chlorophyll alpha concentration) to in situ measurements taken in near-surface waters. The Multi-angle Imaging SpectroRadiometer (MISR) can uniquely constrain the "atmospheric correction" needed to derive ocean color from remote-sensing imagers. Here, we retrieve aerosol amount and type from MISR over all types of water. The primary limitation is an upper bound on aerosol optical depth (AOD), as the algorithm must be able to distinguish the surface. This updated MISR research aerosol retrieval algorithm (RA) also assumes that light reflection by the underlying ocean surface is Lambertian. The RA computes the ocean surface reflectance (R (sub rs)) analytically for a given AOD, aerosol optical model, and wind speed. We provide retrieval examples over shallow, turbid, and eutrophic waters and introduce a productivity and turbidity index (PTI), calculated from retrieved spectral R (sub rs), that distinguished water types (similar to the the normalized difference vegetation index, NDVI, over land). We also validate the new algorithm by comparing spectral AOD and Angstrom exponent (ANG) results with 2419 collocated AErosol RObotic NETwork (AERONET) observations. For AERONET 558 nanometer-interpolated AOD less than 1.0, the root-mean square error (RMSE) is 0.04 and linear correlation coefficient is 0.95. For the 502 cloud-free MISR and AERONET collocations with an AERONET AOD greater than 0.20, the ANG RMSE is 0.25 and r is 0.89. Although MISR RA AOD retrieval quality does not appear to be substantially impacted by the presence of turbid water, the MISR-RA-retrieved Angstrom exponent seems to suffer from increased uncertainty under such conditions. MISR supplements current ocean color sources in regions where sunglint precludes retrievals from single-view-angle instruments. MISR atmospheric correction should also be more robust than that derived from single-view instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS). This is especially true in regions of shallow, turbid, and eutrophic waters, locations where biological productivity can be high, and single-view-angle retrieval algorithms struggle to separate atmospheric from oceanic features.

Satellite Remote Sensing↗
NASA NTRS1986

Delineation of fault zones using imaging radar

The assessment of earthquake hazards and mineral and oil potential of a given region requires a detailed knowledge of geological structure, including the configuration of faults. Delineation of faults is traditionally based on three types of data: (1) seismicity data, which shows the location and magnitude of earthquake activity; (2) field mapping, which in remote areas is typically incomplete and of insufficient accuracy; and (3) remote sensing, including LANDSAT images and high altitude photography. Recently, high resolution radar images of tectonically active regions have been obtained by SEASAT and Shuttle Imaging Radar (SIR-A and SIR-B) systems. These radar images are sensitive to terrain slope variations and emphasize the topographic signatures of fault zones. Techniques were developed for using the radar data in conjunction with the traditional types of data to delineate major faults in well-known test sites, and to extend interpretation techniques to remote areas.

Toksoz, M. N.↗
NASA NTRS2003

Acquisition of Earth Science Remote Sensing Observations from Commercial Sources: Lessons Learned from the Space Imaging IKONOS Example

In an effort to more full explore the potential of commercial remotely sensed land data sources, the NASA Earth Science Enterprise (ESE) implemented an experimental Scientific Data Purchase (SDP) that solicited bids from the private sector to meet ESE-user data needs. The images from the Space Imaging IKONOS system provided a particularly good match to the current ESE missions such as Terra and Landsat 7 and therefore serve as a focal point in this analysis.

Goward, Samuel N.↗
NASA NTRS1989

Characteristics of the GOES I-M imager and sounder

The key features and operational characteristics of the thermal imaging and sounding instruments included into the next-generation GOES spacecraft (GOES I-M) are described. The GOES Imager's censor module has five spectral channels, including an eigth-element visible channel, three IR channels, and a water-vapor channel. The GOES Sounder's detector and filter arrangement makes use of four spectral bands: long-wave, mid-wave, short-wave, and visible. Tables of the Imager and the Sounder sensing performance characteristics are presented together with diagrams of the Imager optic parts and the Imager and the Sounder field and scan patterns.

Koenig, Edward W.↗
NASA NTRS1997

Global Auroral Imaging as a Remote Diagnostic of Geospace

Images of the Earth's aurora, taken from space, can be used to examine plasma behavior throughout the magnetospheric regions surrounding the earth. The coupling of the magnetospheric plasmas through the ionosphere are discussed. A summary of past and current imaging technology is given and then specific examples of remote sensing are given using images from the Ultraviolet Imager aboard the POLAR satellite.

Germany, G. A.↗
NASA NTRS2013

Orbiting Rainbows: Optical Manipulation of Aerosols and the Beginnings of Future Space Construction

Our objective is to investigate the conditions to manipulate and maintain the shape of an orbiting cloud of dust-like matter so that it can function as an ultra-lightweight surface with useful and adaptable electromagnetic characteristics, for instance, in the optical, RF, or microwave bands. Inspired by the light scattering and focusing properties of distributed optical assemblies in Nature, such as rainbows and aerosols, and by recent laboratory successes in optical trapping and manipulation, we propose a unique combination of space optics and autonomous robotic system technology, to enable a new vision of space system architecture with applications to ultra-lightweight space optics and, ultimately, in-situ space system fabrication. Typically, the cost of an optical system is driven by the size and mass of the primary aperture. The ideal system is a cloud of spatially disordered dust-like objects that can be optically manipulated: it is highly reconfigurable, fault-tolerant, and allows very large aperture sizes at low cost. See Figure 1 for a scenario of application of this concept. The solution that we propose is to construct an optical system in space in which the nonlinear optical properties of a cloud of micron-sized particles are shaped into a specific surface by light pressure, allowing it to form a very large and lightweight aperture of an optical system, hence reducing overall mass and cost. Other potential advantages offered by the cloud properties as optical system involve possible combination of properties (combined transmit/receive), variable focal length, combined refractive and reflective lens designs, and hyper-spectral imaging. A cloud of highly reflective particles of micron-size acting coherently in a specific electromagnetic band, just like an aerosol in suspension in the atmosphere, would reflect the Sun's light much like a rainbow. The only difference with an atmospheric or industrial aerosol is the absence of the supporting fluid medium. This new concept is based on recent understandings in the physics of optical manipulation of small particles in the laboratory and the engineering of distributed ensembles of spacecraft clouds to shape an orbiting cloud of micron-sized objects. In the same way that optical tweezers have revolutionized micro- and nano-manipulation of objects, our breakthrough concept will enable new large scale NASA mission applications and develop new technology in the areas of Astrophysical Imaging Systems and Remote Sensing because the cloud can operate as an adaptive optical imaging sensor. While achieving the feasibility of constructing one single aperture out of the cloud is the main topic of this work, it is clear that multiple orbiting aerosol lenses could also combine their power to synthesize a much larger aperture in space to enable challenging goals such as exoplanet detection. Furthermore, this effort could establish feasibility of key issues related to material properties, remote manipulation, and autonomy characteristics of cloud in orbit. There are several types of endeavors (science missions) that could be enabled by this type of approach, i.e. it can enable new astrophysical imaging systems, exoplanet search, large apertures allow for unprecedented high resolution to discern continents and important features of other planets, hyperspectral imaging, adaptive systems, spectroscopy imaging through limb, and stable optical systems from Lagrange-points. Future micro-miniaturization might hold promise of a further extension of our dust aperture concept to other more exciting smart dust concepts with other associated capabilities.

Space Construction↗
NASA NTRSDate not supplied

Satellite-Derived Imagery and Transfer Learning: A Novel Technique for Land Cover Classification

Land cover classification is a continuing research topic due to its relevance to land use and land cover changes from impacts such as climate change, agriculture, urbanization, and hazardous weather. Simple access to frequently changing land cover classifications could provide knowledge and decision support to various researchers and agencies across the globe for each of above-mentioned and related influences. This research aims to provide a novel technique for land cover classification of remote sensing imagery; harnessing Artificial Neural Networks and transfer learning (TL). Knowledge sharing techniques within machine learning are typically utilized when training datasets are sparse or transitions between data modalities is required. In this case, two data modalities, multi and hyperspectral data, are considered for knowledge transfer. The large number of continuous spectral bands available from hyperspectral sensors typically provide increased sophisticated land classification capability compared to more traditional multispectral imagery with limited discrete spectral bands. However, large-scale, frequent access to hyperspectral imagery is relatively limited. The proposed classification technique would therefore prove useful in regions in which hyperspectral data are not readily available for classification but multispectral data are. Image segmentation models are trained on each multi and hyperspectral datasets. Knowledge sharing techniques are then applied to each model to understand what knowledge, if any, is gained when moving between the data modalities. The datasets utilized for training and testing a U-Net model include the European Space Agency’s multispectral imager Sentinel-2 and the hyperspectral German Aerospace Center’s Earth Sensing Imaging Spectrometer (DESIS). Initially, only two classes, land and water, are classified for simplicity. However, more complex classes can be added if knowledge sharing is successful. The workflow for image classification through supervised image segmentation with a U-Net model will be discussed along with metrics calculated before and after TL for both Sentinel-2 and DESIS data are applied. Additionally, future steps to advance the sophistication of this technique as well as other applicable methodologies will be explored.

Emily Foshee↗
NASA NTRS1991

Landsat Legacy

The Landsat resources survey system spawned a number of companies engaged in commercial applications of remote sensing, among them International Imaging Systems (I2S). With initial NASA assistance, I2S has provided remote sensing hardware and software to several foreign countries, developed meteorological analysis systems, medical diagnostic software and scanning equipment for government and commercial use. Latest product is an advanced image-based photogrammetric system employing digital technology - not optical or mechanical systems - to generate terrain elevation data and other processing functions. Called PRI2SM, it compensates automatically for topographic relief displacement, is cheaper, faster, and easier to use and maintain. Company product line includes four major areas: image processing equipment for Earth Resources Management; meteorological analysis systems; satellite ground processing systems; and digital photogrammetric mapping systems.

Source record↗
NASA NTRS1979

Postprocessing classification images

Program cleans up remote-sensing maps. It can be used with existing image-processing software. Remapped images closely resemble familiar resource information maps and can replace or supplement classification images not postprocessed by this program.

Kan, E. P.↗
NASA NTRSDate not supplied

Onboard Hyperspectral Image Classification via Transfer Learning for Communication-Limited Spacecraft

Employing deep-learning and artificial-intelligence (AI) techniques onboard spacecraft can dramatically improve priority data selection to ensure more effective use of the available downlink. However, deployment of effective deep-learning models requires significant training on the ground, which may not be feasible, due to limited data available in an unexplored environment. Therefore, this research explores building robust classification models for onboard data processing where training data is highly limited using transfer-learning techniques. In this paper, we focus on the use case of hyperspectral imaging for remote sensing, a domain where the high dimensionality of the data from the sensor can rapidly saturate the downlink bandwidth. With this bottleneck, there is an impending need to autonomously and robustly classify data onboard to optimize downlink of high-impact measurements, thus maximizing the scientific utility per bit transmitted to the ground. This paper examines the use of deep neural networks onboard for hyperspectral image classification in a communication-limited scenario to analyze how the models perform with limited training data. The use of transfer learning can ameliorate the issue of poor generalization by transferring features learned from training on a large source dataset for one classification task to the target classification task with limited training data. For two deep-learning models from literature, we compare the accuracy of the models trained using transfer learning to models trained from scratch using a random weight initialization with varying amounts of training data. We demonstrate the feasibility and performance of running inference of the deep-learning models on representative flight-like hardware.

Advanced Avionics, Machine Learning, Data Processi↗
NASA NTRS1993

A Role for AVIRIS in the Landsat and Advanced Land Remote Sensing System Program

As a calibrated imaging spectrometer flying at a 20 km altitude, AVIRIS may contribute to the Landsat and the Advanced Land Remote Sensing System efforts. These contributions come in the areas of: 1) on-orbit calibration, 2) specification of new spectral bands, 3) validation of algorithms and 4) investigation of an imaging spectrometer for the Advance Land Remote Sensing System.

AVIRIS↗
NASA NTRS2013

Implementation of a Wavefront-Sensing Algorithm

A computer program has been written as a unique implementation of an image-based wavefront-sensing algorithm reported in "Iterative-Transform Phase Retrieval Using Adaptive Diversity" (GSC-14879-1), NASA Tech Briefs, Vol. 31, No. 4 (April 2007), page 32. This software was originally intended for application to the James Webb Space Telescope, but is also applicable to other segmented-mirror telescopes. The software is capable of determining optical-wavefront information using, as input, a variable number of irradiance measurements collected in defocus planes about the best focal position. The software also uses input of the geometrical definition of the telescope exit pupil (otherwise denoted the pupil mask) to identify the locations of the segments of the primary telescope mirror. From the irradiance data and mask information, the software calculates an estimate of the optical wavefront (a measure of performance) of the telescope generally and across each primary mirror segment specifically. The software is capable of generating irradiance data, wavefront estimates, and basis functions for the full telescope and for each primary-mirror segment. Optionally, each of these pieces of information can be measured or computed outside of the software and incorporated during execution of the software.

Smith, Jeffrey S.↗