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At least 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Assessment of Computer-based Geologic Mapping of Rock Units in the LANDSAT-4 Scene of Northern Death Valley, California

Geologists obtain low accuracy levels when maps derived from LANDSAT MSS data are compared with those made by conventional methods. Procedures developed for the IDIMS computer system and used to classify a subset of a TM image of the Death Valley, California - Nevada border are described. Despite the superior resolution, broader spectral coverage, and greater sensitivity inherent to the TM, the actual recorded measured accuracy was in the same narrow range (30 to 60%) recorded for MSS data from earlier LANDSATs. The supervised classification approach appears to be superior to the unsupervised approach when applied to vegetation-sparse surfaces composed of spectrally contrasting rock/soil units distributed in relatively flat to low relief terrain. As spatial resolution improves and optimal spectral bands for identifying rock materials are specified, use of classified multispectral remote sensing data from air and space when coupled with supporting field calibration and checks should become the dominant way in which geologic mapping is carried out in future decades.

Short, N. M.↗
NASA NTRS1993

Characteristics of arachnoids from Magellan data

Current high resolution Magellan data enables more detailed geological study of arachnoids, first identified by Barsukov et al. as features characterized by a combination of radar-bright, concentric rings and radiating lineations, named 'arachnoids' on the basis of their spider and web-like appearance. Identification of arachnoids in Magellan data has been based on SAR images, in keeping with the original definition. However, there is some overlap by other workers in identification of arachnoids, corona (predominantly bright rings), and novae (predominantly radiating lineations), as all of these features share some common characteristics. Features used in this survey were chosen based on their classification as arachnoids in Head et al.'s catalog and on SAR characteristics matching Barsukov et al.'s original definition. The 259 arachnoids have been currently identified on Venus, all of which were considered in this study. Fifteen arachnoids from different regions, chosen for their 'type' characteristics and lack of deformation by other regional processes, were studied in depth, using SAR and altimetric data to map and profile these arachnoids in an attempt to better determine their geologic and altimetric characteristics and possible formation sequences.

Dawson, C. B.↗
NASA NTRS1993

Classical novae and recurrent novae: General properties

We describe the observable characteristics of classical novae and recurrent novae obtained by different techniques (photometry, spectroscopy, and imaging) in all the available spectral ranges. We consider the three stages in the life of a nova: quiescence (pre- and post-outburst), outburst, final decline and nebular phase. We describe the photometric properties during the quiescent phase. We describe the photometric properties during outburst, the classification according the rate of decline (magnitudes per day), which permits us to define very fast, fast, intermediate, slow, and very slow novae and the correlation between luminosity and speed class. We report the scanty data on the spectra of the few known prenovae and those on the spectra of old novae and those of dwarf novae and nova-like, which, however, are almost undistinguishable. We describe the typical spectra appearing from the beginning of the outburst, just before maximum, up to the nebular phase and the correlation between spectral type at maximum, expansional velocity, and speed class of the nova. We report the existing infrared observations, which permit us to explain some of the characteristics of the outburst light curve, and give evidence of the formation of a dust shell in slow and intermediate novae (with the important exception of the very slow nova HR Del 1967) and its absence or quasi-absence in fast novae. The ultraviolet and X-ray observations are described. The X ray observations of novae, mainly from the two satellites EINSTEIN and EXOSAT, are reported. Observations of the final decline and of the envelopes appearing several months after outburst are also reported.

Hack, Margherita↗
NASA NTRS1995

Remotely sensed image compression based on wavelet transform

In this paper, we present an image compression algorithm that is capable of significantly reducing the vast amount of information contained in multispectral images. The developed algorithm exploits the spectral and spatial correlations found in multispectral images. The scheme encodes the difference between images after contrast/brightness equalization to remove the spectral redundancy, and utilizes a two-dimensional wavelet transform to remove the spatial redundancy. the transformed images are then encoded by Hilbert-curve scanning and run-length-encoding, followed by Huffman coding. We also present the performance of the proposed algorithm with the LANDSAT MultiSpectral Scanner data. The loss of information is evaluated by PSNR (peak signal to noise ratio) and classification capability.

Kim, Seong W.↗
DOE OSTI2025

Accelerated Over-The-Air Neural Receiver Training Using Self-Contrastive Learning

Self-contrastive learning (SCL), a self-supervised learning method, has been shown to improve image and signal classifier accuracies and reduce the training time for neural communications receivers. In particular, prior work has shown that SCL applied as a pre-training step can improve simulated performance of OFDM in 3GPP TDL channel models by reducing the training time of the downstream classification task (demodulation and demapping). In this work a practical implementation demonstrating SCL pre-training using software defined radios (SDRs) is proposed.

Cooke, Corey [ORNL] (ORCID:0000000234263672)↗
NASA NTRS1973

Evaluation of usefulness of Skylab EREP S-190 and S-192 imagery in multistage forest surveys

The author has identified the following significant results. A set of digital interpretation programs have been developed which perform the following tasks: (1) extraction of images for a desired sample unit or combination of sample units by scanner channel and storage of these images on high speed storage devices; (2) extraction of tone and texture information of 8 x 8 pixel interpretation units by channel and storage of these features on a high speed storage device; (3) center of gravity cluster node seeking in higher dimensional space and subsequent nearest neighbor classification, and output of the classification results on a high speed storage device. In all instances the classification results can be related to the original place of subset extraction through the coordinate transformation work previously performed.

Langley, P. G.↗
NASA NTRS2000

PSIDD3: Post-Scan Ultrasonic Data Display System for the Windows-Based PC Including Fuzzy Logic Analysis

Post-Scan Interactive Data Display (PSIDD) III is a user-oriented Windows-based system that facilitates the display and comparison of ultrasonic contact data. The system is optimized to compare ultrasonic measurements made at different locations within a material or at different stages of material degradation. PSIDD III provides complete analysis of the primary wave forms in the time and frequency domains along with the calculation of several frequency dependent properties including Phase Velocity and Attenuation Coefficient and several frequency independent properties, like the Cross Correlation Velocity. The system allows image generation on all of the frequency dependent properties at any available frequency (limited by the bandwidth used in the scans) and on any of the frequency independent properties. From ultrasonic contact scans, areas of interest on an image can be studied with regard to underlying raw waveforms and derived ultrasonic properties by simply selecting the point on the image. The system offers various modes of in-depth comparison between scan points. Up to five scan points can be selected for comparative analysis at once. The system was developed with Borland Delphi software (Visual Pascal) and is based on a SQL database. It is ideal for classification of material properties, or location of microstructure variations in materials.

Lovelace, Jeffrey J.↗
NASA NTRS2003

Classifying Urban Land Covers Using Local Indices of Spatial Complexity

The skewed statistical distributions of land cover types in complex, heterogeneous urban areas limits the effectiveness of traditional spectrally based maximum-likelihood classifiers. This work examines the utility of fractal dimension and Moran's I index of spatial autocorrelation in segmenting high-resolution panchromatic and lower-resolution multispectral imagery. Tools available in the Image Characterization and Modeling System (ICAMS) were used to analyze multi-temporal and multi-platform imagery of Atlanta, Georgia. In this example, land cover change trajectories from forest or grassland to built up land covers lead to decreased spatial autocorrelation. In lower resolution imagery such as Landsat MSS, the complex details of forested land covers and urbanized areas are smoothed, and texture-based change detection is less effective. Although segmentation of panchromatic images is possible using fractal dimension or Moran's I, widely differing land covers often yield similar values of these indices. Better results are obtained when a surface of local fractal dimension or spatial autocorrelation is combined as an additional layer in a supervised maximum-likelihood multispectral classification.

Arumugam, Mahesh↗
NASA NTRS2001

Ultrasonic Data Display and Analysis System Developed (Including Fuzzy Logic Analysis) for the Windows-Based PC

Post-Scan Interactive Data Display (PSIDD) III is a user-oriented Windows-based system that facilitates the display and comparison of ultrasonic contact measurement data obtained at NASA Glenn Research Center's Ultrasonic Nondestructive Evaluation measurement facility. The system is optimized to compare ultrasonic measurements made at different locations within a material or at different stages of material degradation. PSIDD III provides complete analysis of the primary waveforms in the time and frequency domains along with the calculation of several frequency-dependent properties including phase velocity and attenuation coefficient and several frequency-independent properties, like the cross correlation velocity. The system allows image generation on all the frequency-dependent properties at any available frequency (limited by the bandwidth used in the scans) and on any of the frequency-independent properties. From ultrasonic contact scans, areas of interest on an image can be studied with regard to underlying raw waveforms and derived ultrasonic properties by simply selecting the point on the image. The system offers various modes of indepth comparison between scan points. Up to five scan points can be selected for comparative analysis at once. The system was developed with Borland Delphi software (Visual Pascal) and is based on an SQL data base. It is ideal for the classification of material properties or the location of microstructure variations in materials. Along with the ultrasonic contact measurement software that it is partnered with, this system is technology ready and can be transferred to users worldwide.

Lovelace, Jeffrey J.↗
NASA NTRS2011

Current Status of Multi-Angle Implementation of Atmospheric Correction (MAIAC) Algorithm

A new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm has been developed for MODIS. MAIAC uses a time series and an image based rather than pixel-based processing to perform simultaneous retrievals of aerosol properties and surface bidirectional reflectance. It is a generic algorithm which works over all land surface types with the exception of snow. MAIAC has an internal Cloud Mask, a dynamic land-water-snow classification and a surface change mask which allows it to flexibly choose processing path over different surfaces. A distinct feature of MAIAC is a high 1 km resolution of aerosol retrievals including optical thickness and fine mode fraction, which is required in different applications including the air quality analysis. An overview of the algorithm, results of AERONET validation, and examples of comparison with MODIS Collection 5 aerosol product, including Deep Blue algorithm, will be presented for different parts of the world including continental USA, Persian Gulf region and India.

Lyapustin, A.↗
NASA NTRS2017

Automated Mapping of Flood Events in the Mississippi River Basin Utilizing NASA Earth Observations

The Mississippi River Basin is the fourth largest drainage basin in the world, and is susceptible to multi-level flood events caused by heavy precipitation, snow melt, and changes in water table levels. Conducting flood analysis during periods of disaster is a challenging endeavor for NASA's Short-term Prediction Research and Transition Center (SPoRT), Federal Emergency Management Agency (FEMA), and the U.S. Geological Survey's Hazards Data Distribution Systems (USGS HDDS) due to heavily-involved research and lack of manpower. During this project, an automated script was generated that performs high-level flood analysis to relieve the workload for end-users. The script incorporated Landsat 8 Operational Land Imager (OLI) tiles and utilized computer-learning techniques to generate accurate water extent maps. The script referenced the Moderate Resolution Imaging Spectroradiometer (MODIS) land-water mask to isolate areas of flood induced waters. These areas were overlaid onto the National Land Cover Database's (NLCD) land cover data, the Oak Ridge National Laboratory's LandScan data, and Homeland Infrastructure Foundation-Level Data (HIFLD) to determine the classification of areas impacted and the population density affected by flooding. The automated algorithm was initially tested on the September 2016 flood event that occurred in Upper Mississippi River Basin, and was then further tested on multiple flood events within the Mississippi River Basin. This script allows end users to create their own flood probability and impact maps for disaster mitigation and recovery efforts.

Bartkovich, Mercedes↗
NASA NTRSDate not supplied

New York Ecological Forecasting: Utilizing NASA Earth Observations to Map Ash Distribution and Inform Emerald Ash Borer Control

Since their first sightings in the U.S. in 2002, emerald ash borer beetles (Agrilus planipennis; EAB) have killed millions of native ash (Fraxinus spp.) trees across 35 states. Infected ash stands frequently exhibit complete mortality, with the predicted result being the functional extinction of native ash in U.S. forests. In August of 2020, EAB was discovered in the 6.1-million-acre Adirondack Park. The team’s partners at the Adirondack Park Invasive Plant Program (APIPP) desired ash tree distribution and EAB susceptibility information to help improve EAB bio-control efficiency and apply the methodology to future invasive programs. To assist, the team mapped ash tree distribution using NASA Earth observations from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Shuttle Radar Topography Mission (SRTM), along with hyperspectral imagery from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Field data from the Monitoring and Managing Ash (MaMA) project, iMapInvasives and iNaturalist databases, and the New York State Department of Environmental Conservation (NYSDEC) provided ground truthing for mapping and modeling. Results indicate that for ash detection, the team’s Spectral Angle Mapping (SAM) hyperspectral classification is slightly more sensitive but less accurate than multispectral Random Forest (RF) classification, though neither method was above a ~20% detection rate. End products include maps of ash extent derived from both imagery types, a model forecasting future spread scenarios based on current EAB presence, and outreach materials. These products inform APIPP’s management decisions and facilitate public awareness of EAB’s threat to communities within the region.

Liam Megraw↗
NASA NTRS2003

Classification of Mars Pathfinder Rock Surfaces Using Quantitative Morphologic Indices

We have examined the morphology of rocks in two regions of the MPF landing site in terms of location, size and dimensions, sphericity sphericity and elongation, and have correlated this information with spectral data extracted from associated rock surfaces, with the goal of improving the likelihood of discerning between rock and rock surface types. We use four highly diverse Imager for Mars Pathfinder (IMP) images centered on Mini-Matterhorn and the Rock Garden because they are suited to demonstrate the wide morphologic variation of rocks at the site. A sampling of rocks was chosen at these locations that represented a range of shapes, textures and spectral signatures. In this initial analysis we focused upon the largest rocks that are situated in such a way as to allow easy viewing of most of the faces.

Yingst, R. A.↗
NASA NTRS2009

Processing of 3-Dimensional Flash Lidar Terrain Images Generated From an Airborne Platform

Data from the first Flight Test of the NASA Langley Flash Lidar system have been processed. Results of the analyses are presented and discussed. A digital elevation map of the test site is derived from the data, and is compared with the actual topography. The set of algorithms employed, starting from the initial data sorting, and continuing through to the final digital map classification is described. The accuracy, precision, and the spatial and angular resolution of the method are discussed.

Bulyshev, Alexander↗
DOE OSTI2020

Evaluation of Techniques for Vegetation Removal from UAV-Based Photogrammetric DSM near WIPP Land Withdrawal Act Boundary, NM

The Waste Isolation Pilot Plant is located within New Mexico's karst landscape and is the United States' only deep geologic radioactive waste repository which isolates transuranic waste from defense activities underground in a bedded salt formation. Characteristic karst topography in this region is made up of material (such as limestone) that can be dissolved by water over a long period of time, and includes features such as springs, surface streams, sinking streams, caves, and sinkholes. Karst features can potentially impact the integrity of the waste repository in the future and compromise WIPP's performance, particularly when coupled with incompatible land-use activities within the Land Withdrawal Act (LWA) boundary as well as increased water withdrawals outside the LWA. In order to better capture the ground surface topography to support hydrology modeling efforts, the U.S. DOE has enlisted FIU to develop a high-resolution digital elevation model (DEM). In a previous field study, a high-resolution digital surface model (DSM) of an adjacent representative site, Basin 6, was developed by photogrammetry using aerial images captured with unmanned aerial vehicles. The DSM generated was based on point clouds representing the entire landscapes, including points of terrain, vegetation and infrastructure. These above-ground features need to be removed to create a DSM/DEM with good representation of the bare ground. In recent years, many techniques including both pre- and post-processing of DSMs, were developed for vegetation removal with varied success. This research focuses on applying and comparing various vegetation removal methods using point clouds generated of the Basin 6 pilot study area. The 3D RGB-based point cloud classification using Python scripts was a modification of the procedure by Themistocleous (2019). The method using Liblas classifies the point cloud three-dimensionally, whereas the method by Themistocleous (2019) is two-dimensional. The preliminary result of the method using Liblas, however, still had a large amount of vegetation remaining compared to the Pix4D Machine Learning method. The vegetation layer remains highly visible in the DEMs processed using the vegetation indices as opposed to the Pix4D Machine Learning method, which produces a DEM where mostly bare ground is seen. Further modifications to the input parameters are required to improve the vegetation vs. bare ground classification using this approach. The next step will be to improve the method using Liblas and to also test other vegetation removal methods found in the literature review to compare them against the two described here.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2003

A Fast Implementation of the ISOCLUS Algorithm

Unsupervised clustering is a fundamental tool in numerous image processing and remote sensing applications. For example, unsupervised clustering is often used to obtain vegetation maps of an area of interest. This approach is useful when reliable training data are either scarce or expensive, and when relatively little a priori information about the data is available. Unsupervised clustering methods play a significant role in the pursuit of unsupervised classification. One of the most popular and widely used clustering schemes for remote sensing applications is the ISOCLUS algorithm, which is based on the ISODATA method. The algorithm is given a set of n data points (or samples) in d-dimensional space, an integer k indicating the initial number of clusters, and a number of additional parameters. The general goal is to compute a set of cluster centers in d-space. Although there is no specific optimization criterion, the algorithm is similar in spirit to the well known k-means clustering method in which the objective is to minimize the average squared distance of each point to its nearest center, called the average distortion. One significant feature of ISOCLUS over k-means is that clusters may be merged or split, and so the final number of clusters may be different from the number k supplied as part of the input. This algorithm will be described in later in this paper. The ISOCLUS algorithm can run very slowly, particularly on large data sets. Given its wide use in remote sensing, its efficient computation is an important goal. We have developed a fast implementation of the ISOCLUS algorithm. Our improvement is based on a recent acceleration to the k-means algorithm, the filtering algorithm, by Kanungo et al.. They showed that, by storing the data in a kd-tree, it was possible to significantly reduce the running time of k-means. We have adapted this method for the ISOCLUS algorithm. For technical reasons, which are explained later, it is necessary to make a minor modification to the ISOCLUS specification. We provide empirical evidence, on both synthetic and Landsat image data sets, that our algorithm's performance is essentially the same as that of ISOCLUS, but with significantly lower running times. We show that our algorithm runs from 3 to 30 times faster than a straightforward implementation of ISOCLUS. Our adaptation of the filtering algorithm involves the efficient computation of a number of cluster statistics that are needed for ISOCLUS, but not for k-means.

Memarsadeghi, Nargess↗
NASA NTRS1989

Methods and decision making on a Mars rover for identification of fossils

A system for automated fusion and interpretation of image data from multiple sensors, including multispectral data from an imaging spectrometer is being developed. Classical artificial intelligence techniques and artificial neural networks are employed to make real time decision based on current input and known scientific goals. Emphasis is placed on identifying minerals which could indicate past life activity or an environment supportive of life. Multispectral data can be used for geological analysis because different minerals have characteristic spectral reflectance in the visible and near infrared range. Classification of each spectrum into a broad class, based on overall spectral shape and locations of absorption bands is possible in real time using artificial neural networks. The goal of the system is twofold: multisensor and multispectral data must be interpreted in real time so that potentially interesting sites can be flagged and investigated in more detail while the rover is near those sites; and the sensed data must be reduced to the most compact form possible without loss of crucial information. Autonomous decision making will allow a rover to achieve maximum scientific benefit from a mission. Both a classical rule based approach and a decision neural network for making real time choices are being considered. Neural nets may work well for adaptive decision making. A neural net can be trained to work in two steps. First, the actual input state is mapped to the closest of a number of memorized states. After weighing the importance of various input parameters, the net produces an output decision based on the matched memory state. Real time, autonomous image data analysis and decision making capabilities are required for achieving maximum scientific benefit from a rover mission. The system under development will enhance the chances of identifying fossils or environments capable of supporting life on Mars

Eberlein, Susan↗