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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Temporal Interpolation of Geostationary Satellite Imagery With Optical Flow

Applications of satellite data in areas such as weather tracking and modeling, ecosystem monitoring, wildfire detection, and land-cover change are heavily dependent on the tradeoffs to spatial, spectral, and temporal resolutions of observations. In weather tracking, high-frequency temporal observations are critical and used to improve forecasts, study severe events, and extract atmospheric motion, among others. However, while the current generation of geostationary (GEO) satellites has hemispheric coverage at 10-15-min intervals, higher temporal frequency observations are ideal for studying mesoscale severe weather events. In this work, we present a novel application of deep learning-based optical flow to temporal upsampling of GEO satellite imagery. We apply this technique to 16 bands of the GOES-R/Advanced Baseline Imager mesoscale dataset to temporally enhance full-disk hemispheric snapshots of different spatial resolutions from 10 to 1 min. Experiments show the effectiveness of task-specific optical flow and multiscale blocks for interpolating high-frequency severe weather events relative to bilinear and global optical flow baselines. Finally, we demonstrate strong performance in capturing variability during convective precipitation events.

Image processing↗
NASA NTRS1980

Evaluation of multispectral middle infrared aircraft images for lithologic mapping in the East Tintic Mountains, Utah

Six channels of multispectral middle infrared (8 to 14 micron) aircraft scanner data were acquired over the East Tintic mining district, Utah. This area has high relief and moderate vegetation and consists mainly of Tertiary silicic igneous rocks and Paleozoic quartzite and carbonate rocks that have been locally hydrothermally altered. These digital-image data were computer processed to create a color-composite image based on principal component transformations. Color differences in this image are related to the spectral differences in the surface material and allow discrimination of several rock types, depending primarily on their silica content. When combined with a visible and near infrared color-composite image from a previous flight, with limited field checking, it is possible to discriminate quartzite, carbonate rocks, quartz latitic and quartz monzonitic rocks, latitic and monzonitic rocks, silicified altered rocks, argillized altered rocks, and vegetation.

Kahle, A. B.↗
NASA NTRS2003

Synthesis of Multispectral Bands from Hyperspectral Data: Validation Based on Images Acquired by AVIRIS, Hyperion, ALI, and ETM+

Spectral band synthesis is a key step in the process of creating a simulated multispectral image from hyperspectral data. In this step, narrow hyperspectral bands are combined into broader multispectral bands. Such an approach has been used quite often, but to the best of our knowledge accuracy of the band synthesis simulations has not been evaluated thus far. Therefore, the main goal of this paper is to provide validation of the spectral band synthesis algorithm used in the ART software. The next section contains a description of the algorithm and an example of its application. Using spectral responses of AVIRIS, Hyperion, ALI, and ETM+, the following section shows how the synthesized spectral bands compare with actual bands, and it presents an evaluation of the simulation accuracy based on results of MODTRAN modeling. In the final sections of the paper, simulated images are compared with data acquired by actual satellite sensors. First, a Landsat 7 ETM+ image is simulated using an AVIRIS hyperspectral data cube. Then, two datasets collected with the Hyperion instrument from the EO-1 satellite are used to simulate multispectral images from the ALI and ETM+ sensors.

Blonski, Slawomir↗
NASA NTRS1975

Geologic analyses of LANDSAT-1 multispectral imagery of a possible power plant site employing digital and analog image processing

A site in the Great Valley subsection of the Valley and Ridge physiographic province in eastern Pennsylvania was studied to evaluate the use of digital and analog image processing for geologic investigations. Ground truth at the site was obtained by a field mapping program, a subsurface exploration investigation and a review of available published and unpublished literature. Remote sensing data were analyzed using standard manual techniques. LANDSAT-1 imagery was analyzed using digital image processing employing the multispectral Image 100 system and using analog color processing employing the VP-8 image analyzer. This study deals primarily with linears identified employing image processing and correlation of these linears with known structural features and with linears identified manual interpretation; and the identification of rock outcrops in areas of extensive vegetative cover employing image processing. The results of this study indicate that image processing can be a cost-effective tool for evaluating geologic and linear features for regional studies encompassing large areas such as for power plant siting. Digital image processing can be an effective tool for identifying rock outcrops in areas of heavy vegetative cover.

Lovegreen, J. R.↗
NASA NTRS1982

Examples of sequential radar images from Washington, Arizona and Alaska

Radar image time sequences of three areas were obtained to illustrate the capability for change detection over time intervals that range from two to fourteen years. The image sequences show geomorphic changes caused by volcanic eruption at Mount St. Helens, Washington, changes that have resulted from mining activities in the Sierrita Mining District, Arizona, and differences in the Ikpikpuk River drainage area, Alaska, that are mostly dependent on imaging system characteristics. Enhancement, suppression or distortion of features on the images are related in each case to interaction between the illumination geometry of the imaging system and the characteristics of the terrane surface. The radar images are contrasted with Landsat multispectral scanner images to illustrate the importance of resolution and shadowing for feature perception.

Ford, J. P.↗
NASA NTRS1983

Airborne Imaging Spectrometer - An advanced concept instrument

The Airborne Imaging Spectrometer (AIS) is a multispectral infrared imaging instrument utilizing a 32- x 32-element mercury-cadmium-telluride detector array to achieve high spatial resolution. A novel optical design provides high spectral resolution, and, in conjunction with the 32-element spatial resolution, makes optimal use of the capabilities of the area array. The experiment requirements and the details of instrument design, implementation, and calibration are presented. The superior resolution, accuracy, and data acquisition rate of this sensor/instrument concept for reconnaissance and geophysical survey is detailed and plans for upgrading/improving the instrument and future applications of this design concept are discussed.

Labaw, C.↗
NASA NTRS1998

Mars Volatiles and Climate Surveyor (MVACS) Integrated Payload for the Mars Polar Lander Mission

The Mars Volatiles and Climate Surveyor (MVACS) integrated payload for the Mars Polar Lander will be launched in January 1999, with a scheduled landing on Mars' south-polar layered deposits in December 1999. Over the course of its 90-day nominal mission during the martian southern spring and summer seasons, it will make in situ measurements that will provide new insights into the behavior and distribution of martian volatiles, MVACS consists of four major instrument systems: a surface stereo imager (SSI), which will acquire multispectral stereo images of the surface and atmosphere; a 2-m robotic arm (RA), which will dig a O.5-m deep trench and acquire surface and subsurface samples that will be imaged by a focusable robotic arm camera (RAC), which will take close-up images of surface and subsurface samples at a spatial resolution of 21 micron; a meteorology package (MET), which will make the first measurements of surface pressure, temperature, and winds in Mars' southern hemisphere and employ a tunable diode laser (TDL) spectrometer to measure the water-vapor concentration and isotopic composition of CO2 in the martian atmosphere; and a thermal and evolved gas analyzer (TEGA), which will use differential scanning calorimetry and TDL-evolved gas analysis to determine the concentrations of ices, adsorbed volatiles, and volatile-bearing minerals in surface and subsurface soil samples. The unique in situ measurements made by MVACS at its high-latitude landing site will define a number of important aspects of the physical, isotopic, and chemical nature of the martian near-surface and subsurface environment that will be valuable in better understanding Mars meteorites and returned samples, as well as in the search for martian resources that could be utilized by humans.

Paige, D. A.↗
NASA NTRS2020

Disk-resolved Photometric Properties of Pluto and the Coloring Materials across its Surface

A multiwavelength regionally dependent photometric analysis of Pluto's anti-Charon-facing hemisphere using images collected by New Horizons' Multispectral Visible Imaging Camera (MVIC) reveals large variations in the absolute value and spectral slope of the single-scattering albedo. Four regions of interest are analyzed: the dark equatorial belt, Pluto's north pole, nitrogen-rich regions, and the mid-latitude terrains. Regions dominated by volatile ices such as Lowell Regio and Sputnik Planitia present single-scattering albedos of ~0.98 at 492 nm, almost neutral across MVIC's visible wavelength range (400–910 nm), indicating limited contributions from tholin materials. Pluto's dark equatorial regions, informally named Cthulhu and Krun Maculae, have single-scattering albedos of ~0.16 at 492 nm and are the reddest regions. Applying the Hapke radiative transfer model to combined MVIC and Linear Etalon Imaging Spectral Array (LEISA) spectra (400–2500 nm) of Cthulhu Macula and Lowell Regio successfully reproduces the spectral properties of these two regions of dramatically disparate coloration, composition, and morphology. Since this model uses only a single coloring agent, very similar to the Titan-like tholin of Khare et al., to account for all of Pluto's colors, this result supports the Grundy et al. conclusion that Pluto's coloration is the result of photochemical products mostly produced in the atmosphere. Although cosmic rays and extreme ultraviolet photons reach Pluto's surface where they can drive chemical processing, observations of diverse surface colors do not require different chemical products produced in different environments. We report a correction scaling factor in the LEISA radiometric calibration of 0.74 ± 0.05.

Silvia Protopapa↗
NASA NTRS2023

Designing an Observing System to Study the Surface Biology and Geology (SBG) of the Earth in the 2020s

Observations of planet Earth from space are a critical resource for science and society. Satellite measurements represent very large investments and United States (US) agencies organize their effort to maximize the return on that investment. The US National Research Council conducts a survey of Earth science and applications to prioritize observations for the coming decade. The most recent survey prioritized a visible to shortwave infrared imaging spectrometer and a multispectral thermal infrared imager to meet a range of needs for studying Surface Biology and Geology (SBG). SBG will be the premier integrated observatory for observing the emerging impacts of climate change by characterizing the diversity of plant life and resolving chemical and physiological signatures. It will address wildfire risk, behavior, and recovery as well as responses to hazards such as oil spills, toxic minerals in minelands, harmful algal blooms, landslides, and other geological hazards. The SBG team analyzed needed instrument characteristics (spatial, temporal, and spectral resolutions, measurement uncertainty) and assessed the cost, mass, power, volume, and risk of different architectures. We present an overview of the Research and Applications trade-study analysis of algorithms, calibration and validation needs, and societal applications with specifics of substudies detailed in other articles in this special collection. We provide a value framework to converge from hundreds down to three candidate architectures recommended for development. The analysis identified valuable opportunities for international collaboration to increase the revisit frequency, adding value for all partners, leading to a clear measurement strategy for an observing system architecture.

Observing System↗
NASA NTRS1990

Applications of high-resolution remote sensing image data

There are many situations in which the image resolution of satellite data is insufficient to provide the detail required for resource management and environmental monitoring. This paper will focus on applications of high-resolution (0.4 to 10 m) airborne multispectral and imaging spectrometer data acquired in Canada using the MEIS II multispectral line imager and the PMI imaging spectrometer. Applications discussed will include forestry, mapping, and geobotany.

Strome, W. M.↗
NASA NTRS1988

Airborne imaging spectrometer II - The optical design

The Airborne Imaging Spectrometer (AIS I) was a multispectral infrared imaging instrument utilizing a 32 x 32 element mercury-cadmium-telluride detector array. A novel optical design provided high spectral resolution and, in conjunction with the 32-element spatial resolution, made optimal use of the capabilities of the area array. The operational successes of this proof-of-concept instrument, coupled with advances in infrared detector array technology, have provided the impetus and opportunity to upgrade the instrument by replacing the sensor with a newly developed 64 x 64 detector. The original instrument configuration is reviewed, and the design considerations, limitations and trade-offs necessitated by the new detector are examined, particularly the near doubling of the field of view and the increase from 1024 to 4096 pixels per read-out frame.

La Baw, Clayton↗
NASA NTRS1991

The least-squares mixing models to generate fraction images derived from remote sensing multispectral data

Constrained-least-squares and weighted-least-squares mixing models for generating fraction images derived from remote sensing multispectral data are presented. An experiment considering three components within the pixels-eucalyptus, soil (understory), and shade-was performed. The generated fraction images for shade (shade image) derived from these two methods were compared by considering the performance and computer time. The derived shade images are related to the observed variation in forest structure, i.e., the fraction of inferred shade in the pixel is related to different eucalyptus ages.

Shimabukuro, Yosio Edemir↗
NASA NTRS1976

Evaluation of LANDSAT-2 (ERTS) images applied to geologic structures and mineral resources of South America

The author has identified the following significant results. The Salar of Coposa is located in northern Chile along the frontier with Bolivia. The surface was divided into six general classes of materials. Analysis of LANDSAT image 1243-14001 by use of interactive multispectral computer (Image 100) enabled accurate repetition of these general classes based on reflectance. The Salar of Uyuni is the largest of the South American evaporite deposits. Using image 1243-13595, and parallel piped computer classification of reflectance units, the Salar was divided into nine classes ranging from deep to shallow water, water over salt, salt saturated with water, and several classes of dry salt.

Carter, W. D.↗
NASA NTRS1982

An automatic agricultural zone classification procedure for crop inventory satellite images

A classification procedure for assessing crop areal proportion in multispectral scanner image is discussed. The procedure is into four parts: labeling; classification; proportion estimation; and evaluation. The procedure also has the following characteristics: multitemporal classification; the need for a minimum field information; and verification capability between automatic classification and analyst labeling. The processing steps and the main algorithms involved are discussed. An outlook on the future of this technology is also presented.

Parada, N. D. J.↗
NASA NTRS2005

Large Multispectral and Albedo Panoramas Acquired by the Pancam Instruments on the Mars Exploration Rovers Spirit and Opportunity

Introduction. The panoramic camera (Pancam) multispectral, stereoscopic imaging systems on the Mars Exploration Rovers Spirit and Opportunity [1] have acquired and downlinked more than 45,000 images (~35 Gbits of data) over more than 700 combined sols of operation on Mars as of early January 2005. A large subset of these images were acquired as part of 26 large multispectral and/or broadband "albedo" panoramas (15 on Spirit, 11 on Opportunity) covering large ranges of azimuth (12 spanning 360 ) and designed to characterize major regional color and albedo characteristics of the landing sites and various points along both rover traverses.

Bell, J. F., III↗
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↗