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At least 253 records · Page 14

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NASA NTRS1982

A means for utilizing ancillary information in multispectral classification

Attention is given to a method which makes it possible to incorporate information from ancillary data sources into the results of an existing classification of remotely sensed data. The method is based upon probabilistic label relaxation procedures which are used for imbedding spatial context data in image-labeling problems. The source of ancillary information is utilized in the form of a set of probabilities. These are introduced into a modified relaxation method called supervised relaxation labeling which, on application, develops a labeling for remotely sensed data that strikes a balance in consistency between spectral, spatial, and ancillary data sources of information. A description is given of results of a forestry classification in which accuracy is improved from 68% to 81% by incorporating topographic elevation in the outlined manner.

Richards, J. A.↗

NASA NTRS2005

Geographic Information Systems and Martian Data: Compatibility and Analysis

Planning future landed Mars missions depends on accurate, informed data. This research has created and used spatially referenced instrument data from NASA missions such as the Thermal Emission Imaging System (THEMIS) on the Mars Odyssey Orbiter and the Mars Orbital Camera (MOC) on the Mars Global Surveyor (MGS) Orbiter. Creating spatially referenced data enables its use in Geographic Information Systems (GIS) such as ArcGIS. It has then been possible to integrate this spatially referenced data with global base maps and build and populate location based databases that are easy to access.

Jones, Jennifer L.↗

NASA NTRS2006

Improvement in Recursive Hierarchical Segmentation of Data

A further modification has been made in the algorithm and implementing software reported in Modified Recursive Hierarchical Segmentation of Data (GSC- 14681-1), NASA Tech Briefs, Vol. 30, No. 6 (June 2006), page 51. That software performs recursive hierarchical segmentation of data having spatial characteristics (e.g., spectral-image data). The output of a prior version of the software contained artifacts, including spurious segmentation-image regions bounded by processing-window edges. The modification for suppressing the artifacts, mentioned in the cited article, was addition of a subroutine that analyzes data in the vicinities of seams to find pairs of regions that tend to lie adjacent to each other on opposite sides of the seams. Within each such pair, pixels in one region that are more similar to pixels in the other region are reassigned to the other region. The present modification provides for a parameter ranging from 0 to 1 for controlling the relative priority of merges between spatially adjacent and spatially non-adjacent regions. At 1, spatially-adjacent-/spatially- non-adjacent-region merges have equal priority. At 0, only spatially-adjacent-region merges (no spectral clustering) are allowed. Between 0 and 1, spatially-adjacent- region merges have priority over spatially- non-adjacent ones.

Tilton, James C.↗

NASA NTRS2013

A Lightweight I/O Scheme to Facilitate Spatial and Temporal Queries of Scientific Data Analytics

In the era of petascale computing, more scientific applications are being deployed on leadership scale computing platforms to enhance the scientific productivity. Many I/O techniques have been designed to address the growing I/O bottleneck on large-scale systems by handling massive scientific data in a holistic manner. While such techniques have been leveraged in a wide range of applications, they have not been shown as adequate for many mission critical applications, particularly in data post-processing stage. One of the examples is that some scientific applications generate datasets composed of a vast amount of small data elements that are organized along many spatial and temporal dimensions but require sophisticated data analytics on one or more dimensions. Including such dimensional knowledge into data organization can be beneficial to the efficiency of data post-processing, which is often missing from exiting I/O techniques. In this study, we propose a novel I/O scheme named STAR (Spatial and Temporal AggRegation) to enable high performance data queries for scientific analytics. STAR is able to dive into the massive data, identify the spatial and temporal relationships among data variables, and accordingly organize them into an optimized multi-dimensional data structure before storing to the storage. This technique not only facilitates the common access patterns of data analytics, but also further reduces the application turnaround time. In particular, STAR is able to enable efficient data queries along the time dimension, a practice common in scientific analytics but not yet supported by existing I/O techniques. In our case study with a critical climate modeling application GEOS-5, the experimental results on Jaguar supercomputer demonstrate an improvement up to 73 times for the read performance compared to the original I/O method.

Temporal Queries↗

NASA NTRS2006

Using Heaps in Recursive Hierarchical Segmentation of Data

A modification to increase processing speed has been made in the algorithm and implementing software reported in Modified Recursive Hierarchical Segmentation of Data (GSC-14681-1), NASA Tech Briefs, Vol. 30, No. 6 (June 2006), page 51. That software performs recursive hierarchical segmentation of data having spatial characteristics (e.g., spectral-image data). The segmentation process includes an iterative subprocess, in each iteration of which it is necessary to determine a best pair of regions to merge [merges being justified by one or more measure(s) similarity of pixels in the regions]. In the previously reported version of the algorithm and software, the choice of a best pair of regions to merge involved the use of a fully sorted list of regions. That version was computationally inefficient because a fully sorted list is not needed: what is needed is only the identity of the pair of regions characterized by the smallest measure of dissimilarity. The present modification replaces the use of a fully sorted list with the use of data heaps, which are computationally more efficient for performing the required comparisons among dissimilarity measures. The modification includes the incorporation of standard and modified functions for creating and updating data heaps

Tilton, James C.↗

NASA NTRS1993

Linear mixing model applied to AVHRR LAC data

A linear mixing model was applied to coarse spatial resolution data from the NOAA Advanced Very High Resolution Radiometer. The reflective component of the 3.55 - 3.93 microns channel was extracted and used with the two reflective channels 0.58 - 0.68 microns and 0.725 - 1.1 microns to run a Constraine Least Squares model to generate vegetation, soil, and shade fraction images for an area in the Western region of Brazil. The Landsat Thematic Mapper data covering the Emas National park region was used for estimating the spectral response of the mixture components and for evaluating the mixing model results. The fraction images were compared with an unsupervised classification derived from Landsat TM data acquired on the same day. The relationship between the fraction images and normalized difference vegetation index images show the potential of the unmixing techniques when using coarse resolution data for global studies.

Holben, Brent N.↗

NASA NTRS2010

Multiple Spectral-Spatial Classification Approach for Hyperspectral Data

A .new multiple classifier approach for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region, with the corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker selection procedure, each of them combining the results of a pixel-wise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification -driven marker and forms a region in the spectral -spatial classification: map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies, when compared to previously proposed classification techniques.

Tarabalka, Yuliya↗

NASA NTRS2006

Spatial Growth Modeling and High Resolution Remote Sensing Data Coupled with Air Quality Modeling to Assess the Impact of Atlanta, Georgia on the Local and Regional Environment

The growth of cities, both in population and areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 60 percent of the world s population will live in cities. Urban expansion has profound impacts on a host of biophysical, environmental, and atmospheric processes within an urban ecosystems perspective. A reduction in air quality over cities is a major result of these impacts. Because of its complexity, the urban landscape is not adequately captured in air quality models such as the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban spatial growth modeling (SGM) projections as improved inputs to a meteorological/air quality modeling system focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include business as usual and smart growth scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, which in turn, are used to model how air temperature can potentially be moderated as impacts on elevating ground-level ozone, as opposed to not utilizing heat island mitigation strategies. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the CMAQ modeling schemes. Use of these data has been found to better characterize low density/suburban development as compared with USGS lkm land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the Georgia Environmental Protection Division to evaluate how these transportation plans will affect future air quality. The coupled SGM and air quality modeling approach provides insight on what the impacts of Atlanta s growth will be on the local and regional environment and exists as a mechanism that can be used by policy makers to make rational decisions on urban growth and sustainability for the metropolitan area in the future.

Quattrochi, Dale A.↗

NASA NTRS1980

The development of a spectral-spatial classifier for earth observational data

Over the last several years a classifier for earth observational image data has been under development intended to achieve improved performance by utilizing spatial characteristics of the data as an adjunct to multispectral properties. The paper provides an overview of the conception, development, evaluation and documentation of this spectral-spatial classifier. The research program leading to this classifier is described, the algorithms of the current implementation called ECHO are outlined, and results on its performance are summarized. These results show improved accuracy, with greater computation efficiency, and only slightly increased operator complexity.

Landgrebe, D. A.↗

NASA NTRS1974

Combined spectral and spatial processing of ERTS imagery data

A general procedure is presented for extracting textural properties of blocks of image data. These features are calculated in the spatial domain, taking into account the statistical nature of texture. The procedure is based on the assumption that the texture information in an image is contained in the overall or 'average' spatial relationship which the gray tones in the image have to one another. The spatial gray-tone dependence matrix is discussed together with the spectral features used in the study and the land use classification studies conducted. The results of the study show the usefulness of using both spectral and textural characteristics of ERTS multispectral scanner data for developing classification procedures.

Haralick, R. M.↗

NASA NTRSDate not supplied

Goniometric and Polarized Imaging Spectroscopic Lab Measurements ofSpacecraft Materials

To better characterize the spectral response of common spacecraft materials, the following laboratory measurements are presented to support the Space Situational Awareness community in the analysis of remotely sensed observational data. Of interest is classifying material reflective properties using spectral bidirectional reflectance distribution function (BRDF) data and spatially resolved polarized imaging spectroscopy, allowing laboratory data to be applicable to ground-based optical telescope observations. The team acquired a typical CubeSat solar panel and a sample of multi-layer insulation commonly used on spacecraft for initial measurements. The data were collected at the Goniometer of the Rochester Institute of Technology (GRIT) laboratory with a field and laboratory goniometer housing two Analytical Spectral Device (ASD) spectrometers and a Headwall micro-Hyperspec E-Series imaging spectrometer with an adjustable linear polarizer. The goniometer provides spectral reflectance over a broad spectral range from 350-2500 nm at 1 nm spacing with 3 nm spectral resolution in the visible and near infrared and 8 nm in the shortwave infrared. The Headwall imager covers a spectral range from 400-1000 nm with 1.6 nm spectral resolution. We present the results from these initial measurements that show highly reflective regions at various locations in the angular domain for both materials. In addition, the solar cell spectra exhibited strong interference effects typically observed with thin films. Our team is pursuing a variety of typical solar cells to assess variations in product type. The spatially resolved polarization ratio maps show variability across the materials due to surface structure and varying material composition. Based on these results, we outline a plan for simulating spectral radiance light curves of the materials in various orbital configurations as they would be measured from ground- based telescopes for a clear observing sky during twilight. The paper will also present a plan for expanding ours of interest to determine if the results presented are unique to these samples and to categorize the spectral response for different material classes.

Chris H. Lee↗

NASA NTRS1989

Color composite processing of multi-wavelength, multi-polarization airborne radar imagery

Multiwavelength, multipolarization radar image data can be processed to allow the clear depiction of most information in color composite displays. The first principal component, depicted as variations in image intensity, captures the dominant variation in the data and reduces noise without degradation of spatial detail. Other principal components, depicted primarily as variations in image chromaticity, are derived from spatially filtered data so that image colors can be attributed to significant wavelength and polarization contrasts, and not to speckle. Processing paths can be designed to emphasize wavelength contrasts, polarization contrasts, or overall discrimination of surface features.

Crippen, Robert E.↗

NASA NTRS2006

Airborne Turbulence Detection System Certification Tool Set

A methodology and a corresponding set of simulation tools for testing and evaluating turbulence detection sensors has been presented. The tool set is available to industry and the FAA for certification of radar based airborne turbulence detection systems. The tool set consists of simulated data sets representing convectively induced turbulence, an airborne radar simulation system, hazard tables to convert the radar observable to an aircraft load, documentation, a hazard metric "truth" algorithm, and criteria for scoring the predictions. Analysis indicates that flight test data supports spatial buffers for scoring detections. Also, flight data and demonstrations with the tool set suggest the need for a magnitude buffer.

Hamilton, David W.↗

NASA NTRS1986

Army technology development. IBIS query. Software to support the Image Based Information System (IBIS) expansion for mapping, charting and geodesy

The Image Based Information System (IBIS) has been under development at the Jet Propulsion Laboratory (JPL) since 1975. It is a collection of more than 90 programs that enable processing of image, graphical, tabular data for spatial analysis. IBIS can be utilized to create comprehensive geographic data bases. From these data, an analyst can study various attributes describing characteristics of a given study area. Even complex combinations of disparate data types can be synthesized to obtain a new perspective on spatial phenomena. In 1984, new query software was developed enabling direct Boolean queries of IBIS data bases through the submission of easily understood expressions. An improved syntax methodology, a data dictionary, and display software simplified the analysts' tasks associated with building, executing, and subsequently displaying the results of a query. The primary purpose of this report is to describe the features and capabilities of the new query software. A secondary purpose of this report is to compare this new query software to the query software developed previously (Friedman, 1982). With respect to this topic, the relative merits and drawbacks of both approaches are covered.

Friedman, S. Z.↗

NASA NTRS1977

Comparing soil boundaries delineated by digital analysis of multispectral scanner data from high and low spatial resolution systems

The author has identified the following significant results. Computer-aided analysis techniques used with aircraft MSS data showed that the spatial resolution was sufficient to recognize each soil mapping unit of the test site. Some difficulties occurred where different soil series were intricately mixed, and this mixture showed as a separate spectral mapping unit, or where the difference between two soils depended on the depth of silty surface material. Analysis of LANDSAT data with computer-aided techniques showed that it was not possible to find spectrally homogeneous soil features of the seven soil series on the 40 ha test site on the digital display or on a picture print map. Cluster techniques could be used on an extended test area to group spectrally similar data points into cluster classes.

Kristof, S. J.↗

NASA NTRS2004

Spatially Complete Surface Albedo Data Sets: Value-Added Products Derived from Terra MODIS Land Products

Spectral land surface albedo is an important parameter for describing the radiative properties of the Earth. Accordingly it reflects the consequences of natural and human interactions, such as anthropogenic, meteorological, and phenological effects, on global and local climatological trends. Consequently, albedos are integral parts in a variety of research areas, such as general circulation models (GCMs), energy balance studies, modeling of land use and land use change, and biophysical, oceanographic, and meteorological studies. Recent observations of diffuse bihemispherical (white-sky) and direct beam directional hemispherical (black-sky ) land surface albedo included in the MOD43B3 product from MODIS instruments aboard NASA's Terra and Aqua satellite platforms have provided researchers with unprecedented spatial, spectral, and temporal characteristics. Cloud and seasonal snow cover, however, curtail retrievals to approximately half the global land surfaces on an annual equal-angle basis, precluding MOD43B3 albedo products from direct inclusion in some research projects and production environments.

Moody, Eric G.↗

NASA NTRS2021

Learning spatial response functions from large multi-sensor AIRS and MODIS datasets

We use large datasets from the Atmospheric Infrared Sounder (AIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive AIRS spatial response functions and study their potential variations over the mission. The new reconstructed spatial response functions can be used to reduce errors in the radiances in non-uniform scenes and improve products generated using both AIRS and MODIS data. AIRS spatial response functions are distinct for each of its 2378 channels and each of its 90 scan angles. We develop the mathematical model and the optimization framework for deriving spatial response functions for two AIRS channels with low water vapor absorption and various scan angles. We quantify uncertainties in the derived reconstructions and study how they differ from pre-flight spatial response functions. We show that our approach generates reconstructions that agree with the data more accurately compared to pre-flight spatial responses. We derive spatial response functions using data collected during successive dates in order to ascertain the repeatability of the reconstructed spatial response functions. We also compare the derived spatial response functions based on data collected in the beginning, the middle, and at the current state of the mission in order to study changes in reconstructions over time.

Vese, Luminita↗

NASA NTRS1985

The effects of sensor advancements on Thematic Mapper data classification

Analyses of Landsat Thematic Mapper (TM) data were conducted to assess the effects of sensor advancements on the thematic classification of remote sensing data. The effects of altering three sensor characteristics (spatial resolution, data quantization, and spectral band configuration) from Landsat Multispectral Scanner (MSS) specifications were investigated using analysis-of-variance (ANOVA). Analyses were conducted on data from two TM scenes: Washington, D.C. (late autumn) and western Pennsylvania (late summer). Results indicate that the contribution of sensor advancements to thematic classification are highly dependent of spectral and spatial scene attributes.

Irons, J. R.↗