Broadband Spectral Modelling of the Galactic Globular Cluster 47 Tucanae
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79 ASTRONOMY AND ASTROPHYSICS↗
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Results for “spectral clustering”
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A study of the tolerance block approach to special stratification
The author has identified the following significant results. Twelve winter wheat LACIE segments in Kansas were used to compare the performance of three clustering methods: (1) BCLUST, which uses a spectral distance function to accumulate clusters; (2) blocks-alone, which divides spectral space into equally populated blocks; and (3) block-seeds, which uses spectral means of blocks-alone as seeds for accumulating distance-type clusters. Both BCLUST and block-seeds performed equally well and outperformed blocks-alone significantly. Their average variance ratio of about 0.5 showed imperfect separation of wheat from non-wheat. This result points to the need to explore the achievable crop separability in the spectral/temporal domain, and suggest evaluating derived features rather than data channels as a means to achieve purer spectral strata.
Richardson, W.↗
Mimas: Preliminary Evidence For Amorphous Water Ice from VIMS
We have conducted a statistical clustering analysis (1,2) on a mosaic of VIMS data cubes obtained on February 13, 2010, for Saturn s satellite Mimas. Seven VIMS cubes were geometrically projected and re-sampled to a common spatial resolution. The clustering technique consists of a partitioning algorithm coupled to a criterion that prevents sub-optimal solutions and tests for the influence of random noise in the measurements. The clustering technique is agnostic about the meaning of the clusters, and scientific interpretation requires their a posteriori evaluation. The preliminary results yielded five clusters, demonstrating that spectral variability across Mimas surface is statistically significant. The ratios of the means calculated for each of the clusters show structure within the 1.6- micron water ice band, as well as the shape and the central wavelength of the strong ice band at 2 micron, that map spatially in patterns apparently related to the topography of Mimas, in particular certain regions in and around Herschel crater. The mean spectra of the five clusters, show similarities with laboratory spectra of amorphous and crystalline H2O ice (3) that are suggestive of the presence of an amorphous ice component in certain regions of Mimas, notably on the central peak of Herschel, on the crater floor, and in faults surrounding the crater. This may represent a mixture of both ice phases, or perhaps a layer of amorphous ice on a base of crystalline ice. Another possible occurrence of amorphous ice appears southwest of Herschel, close to the south pole.
Cruikshank, Dale P.↗
Observations of rich clusters of galaxies at metre wavelengths
Observations have been made at 10 frequencies between 50 and 120 MHz of 17 rich, X-ray emitting clusters of galaxies with the 78 x 156 m dipole array al Llanherne. The observed flux densities were compared to the flux densities expected on the basis of the known discrete sources in the fields. In no case was a significant flux excess found that might have indicated the presence of a diffuse halo component of radio emission in the cluster. For those clusters in which spectral indices could be determined, the spectra all tend to be much steeper than is normal for extragalactic radio sources, although a strict correlation between the X-ray luminosity and the low-frequency radio luminosity or spectral index is not found. The occurrence of large halo sources such as that which is present in the Coma cluster seems to be quite unusual.
Cane, H. V.↗
3C 323.1 - A QSO in a rich cluster of galaxies.
The redshift and structural data on the compact cluster Zw Cl 1545.1+2104 have been determined from deep plates of the cluster region and from spectral scans of the central galaxy in the cluster. The cluster is of richness 1 on Abell's (1958) scale, and the redshift of its central galaxy agrees well with that of the quasar 3C 323.1 which lies at a projected distance of 6.5 sec from the center of the cluster. The data indicate that the quasar is a member of the cluster as opposed to the observed trend.
Oemler, A., Jr.↗
HEAO-A2 observations of the X-ray spectra of the Centaurus and A1060 clusters of galaxies
The X-ray spectral observations of two low luminosity clusters of galaxies, Centaurus and A1060, are presented. An emission feature of the Centaurus cluster at 7.9 keV is detected at about one third of the strength of the 6.7 keV line. This higher energy line represents K sub beta emission from highly ionized iron. An isothermal model with an Fe emission line is discussed and it is shown that the model cannot fit the data of the Centaurus or the A1060 clusters. The implications of the two component nature of the continuum on the Fe abundance and the X-ray surface brightness distribution are discussed.
Mitchell, R.↗
Partitioning of Large-Scale Power Electronics-Based Power Systems for Small-Signal Stability Analysis
The nodal admittance matrix (NAM)-based approach is suitable for analyzing the small-signal stability of large-scale power electronics-based power systems (PEPSs) as it preserves the system structure by utilizing the admittance matrix. Previously, NAM-based area partition has been proposed, which divides the system into various subareas and interconnections for easier analysis of the low-dimension matrix compared to the entire system-based high-dimension matrix. However, no partition algorithm has been presented for the NAM-based area partition method. This paper focuses on implementing the spectral partitioning algorithm for partitioning large-scale PEPSs into a low-dimension matrix to reduce the computation complexity of the analysis. These spectral components facilitate data transformation into a new space, enabling the application of traditional clustering methods like k-means. To evaluate the performance of the partitioning method, the subareas and interconnections obtained from the spectral clustering algorithm are incorporated into the NAM-based area partition method for a large system with 140 buses. The computational times of the original method, where the NAM-based criterion is directly applied to the entire system, are compared with those of the NAM-based partition method in MATLAB. PSCAD simulations of the whole system and the obtained subareas are conducted to validate the effectiveness of the proposed algorithm.
Nupur, Nupur↗
PAH Clusters as Interstellar Very Small Grains
PAH clusters are one candidate species for the interstellar "very small grains" or "VSGs", i.e., dust grains small enough to be stochastically heated and contribute to the aromatic infrared emission bands (AIBs). This possibility motivated laboratory experiments on the infrared spectroscopy of PAH clusters using matrix isolation spectroscopy. The spectral shifts due to PAH clustering in argon matrices provide clues for the AIB contribution from PAH clusters in the interstellar medium. Here we review results from a number of small PAH species, extrapolation to the much larger PAHs believed to be present in the interstellar medium, and the implications for a PAH cluster contribution to the VSG population.
AIBs↗
HEAO A-2 observations of the X-ray spectra of the Centaurus and A1060 clusters of galaxies
X-ray spectral observations of two nearby low-luminosity clusters of galaxies are presented. For the Centaurus cluster an emission feature at 7.9 keV is detected at about one-third of the strength of the 6.7 keV line. This higher energy line represents K-beta emission from highly ionized iron. In addition, it is demonstrated that for neither the Centaurus nor the A1060 cluster can an isothermal model with an Fe emission line adequately fit the data. Instead, the simplest models which provide acceptable fits include a second, harder component which may be either a second exponential or a power law. The implications of the two-component nature of the continuum on the Fe abundance and the X-ray surface-brightness distribution are discussed.
Mitchell, R.↗
Deciphering the Spectra of Flowers to Map Landscape-scale Blooming Dynamics
Like leaves, floral coloration is driven by inherent optical properties, which are determined by pigments, scattering structure, and thickness. However, establishing the relative contribution of these factors to canopy spectral signals is usually limited to in-situ observations. Modeling flowering dynamics (e.g., blooming duration, spatial distribution) at the landscape scale may reveal insights into ecological processes and phenological adaptations to environmental changes. Multitemporal visible to shortwave infrared (VSWIR) imaging spectroscopy observations are especially suited for such efforts. Reflectance in this spectral range is sensitive to major flower pigments, flowering phenology traces, and biophysical differences between flowers and other plant parts. We explored how flowers contribute to spectral signals using a time series of imagery from the Airborne Visible InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) collected as part of the SBG High-Frequency Time Series (SHIFT) campaign as a case study. Airborne data were collected weekly during the spring of 2022 across two natural reserves in California. Field spectra were gathered from blooming plots at leaf, flower, and canopy levels at two time points during the campaign. The processed data was used to investigate flowering species' spectro-temporal variation and spatial distribution using Spectral Mixture Residual, Gaussian clustering techniques, and a proposed narrow-band flowering index. Linear spectral unmixing allowed the computation of the weighted contribution of four major high-variance endmembers (leaves, flowers, soil, dark) and low-variance residual signal that comprises subtle spectral features used to track biophysical processes. The reflectance residual was projected on a low principal component basis to characterize flowering clusters' variation and spatial distribution based on the Gaussian mixture model, providing an uncertainty metric to assess the results. Mapping flowering events from modeling spectro-temporal dynamics throughout the season, from pre-blooming to post-flowering stages, allowed us to identify gradient variations in spectral features within the VSWIR spectral range linked to flowering pigments. Time series of the Mixture Residual Blooming Index and the Red-Edge Normalized Difference Vegetation Index revealed specific flowering and greenness phenophases across the two main species (Coreopsis gigantea, Artemisia californica) in the flowering areas. Overall, our approach opens opportunities for future satellite monitoring of floral cycles at broader scales.
Yoseline Angel↗
Spectral constraints on models of gas in clusters of galaxies
The HEAO 1A2 spectra of clusters of galaxies are used to determine the temperature profile which characterizes the X-ray emitting gas. Strong evidence of nonisothermality is found for the Coma, A85, and A1795 clusters. Properties of the cluster potential which binds the gas are calculated for a range of model parameters. The typical binding mass, if the gas is adiabatic, is 2-4E14 solar masses and is quite centrally concentrated. In addition, the Fe abundance in Coma is .26 + or - .06 solar, less than the typical value (.5) found for rich clusters. The results for the gas in Coma may imply a physical description of the cluster which is quite different from what was previously believed.
Henriksen, M. J.↗
Spectral constraints on models of gas in clusters of galaxies
The HEAO 1A2 spectra of clusters of galaxies are used to determine the temperature profile which characterizes the X-ray emitting gas. Strong evidence of nonisothermality is found for the Coma, A85, and A1795 clusters. Properties of the cluster potential which binds the gas are calculated for a range of model parameters. The typical binding mass, if the gas is adiabatic, is 2-4E14 solar masses and is quite centrally concentrated. In addition, the Fe abundance in Coma is .26 + or - .06 solar, less than the typical value (.5) found for rich clusters. The results for the gas in Coma may imply a physical description of the cluster which is quite different from what was previously believed.
Henriksen, M. J.↗
Mapping forest vegetation with ERTS-1 MSS data and automatic data processing techniques
This study was undertaken with the intent of elucidating the forest mapping capabilities of ERTS-1 MSS data when analyzed with the aid of LARS' automatic data processing techniques. The site for this investigation was the Great Dismal Swamp, a 210,000 acre wilderness area located on the Middle Atlantic coastal plain. Due to inadequate ground truth information on the distribution of vegetation within the swamp, an unsupervised classification scheme was utilized. Initially pictureprints, resembling low resolution photographs, were generated in each of the four ERTS-1 channels. Data found within rectangular training fields was then clustered into 13 spectral groups and defined statistically. Using a maximum likelihood classification scheme, the unknown data points were subsequently classified into one of the designated training classes. Training field data was classified with a high degree of accuracy (greater than 95 percent), and progress is being made towards identifying the mapped spectral classes.
Messmore, J.↗
Joint Classification and Data Compression of multidimensional information sources - Application to ERTS
This paper describes a concept of Joint Classification and Data Compression of multidimensional information sources in the context of applications to Earth Resources Technology Satellites. The concept is used to define an Information Extraction System intended to remove data redundancy early in the sensor to user link in both classification and film interpretation applications. The approach uses cascaded partially supervised clustering to extract spectral intensity features from spatially local sources. Data compression is then used to efficiently represent spatial features within the spectral intensity feature map.
Hilbert, E. E.↗
Mapping forest vegetation with ERTS-1 MSS data and automatic data processing techniques
This study was undertaken with the intent of elucidating the forest mapping capabilities of ERTS-1 MSS data when analyzed with the aid of LARS' automatic data processing techniques. The site for this investigation was the Great Dismal Swamp, a 210,000 acre wilderness area located on the Middle Atlantic coastal plain. Due to inadequate ground truth information on the distribution of vegetation within the swamp, an unsupervised classification scheme was utilized. Initially pictureprints, resembling low resolution photographs, were generated in each of the four ERTS-1 channels. Data found within rectangular training fields was then clustered into 13 spectral groups and defined statistically. Using a maximum likelihood classification scheme, the unknown data points were subsequently classified into one of the designated training classes. Training field data was classified with a high degree of accuracy (greater than 95%), and progress is being made towards identifying the mapped spectral classes.
Messmore, J.↗
Land-cover classification of California using mosaicking and high-speed processing
A land cover classification of Landsat MSS data has been made for the entire state of California. Digital mosaicking of 32 Landsat scenes with registered digital elevation, slope, and aspect was prepared. Stratified unsupervised clustering generated the spectral classes which were identified into 16 categories, mostly forest types, using U-2 CIR photos. A verification using 81 primary sample units resulted in a forest/nonforest accuracy of 80.2% and a commercial conifer and hardwood accuracy of 75.1%. The results are now being used in many projects throughout the state.
Peterson, D. L.↗
Procedure 1 and forestland classification using Landsat data
Procedure 1 (P-1) was developed for the Large Area Crop Inventory Experiment (LACIE) and has been used extensively to develop land-use classification of agricultural areas. The P-1 approach requires that pixels (also called dots) of known identity must be located in the study scene. The entire area is clustered and the spectral classes formed are identified using the dots. The analyst need only locate and identify the dots. The rest of the work is done by the computer. The objective of the reported study was to evaluate the effectiveness of P-1's automated approach in a complex forest-land situation. The study site was located in the eastern half of the San Juan National Forest in southwestern Colorado. The study showed that P-1 performed as well as the Multicluster Blocks approach on the rugged study area.
Nelson, R. F.↗
Analysis of the Tanana River Basin using LANDSAT data
Digital image classification techniques were used to classify land cover/resource information in the Tanana River Basin of Alaska. Portions of four scenes of LANDSAT digital data were analyzed using computer systems at Ames Research Center in an unsupervised approach to derive cluster statistics. The spectral classes were identified using the IDIMS display and color infrared photography. Classification errors were corrected using stratification procedures. The classification scheme resulted in the following eleven categories; sedimented/shallow water, clear/deep water, coniferous forest, mixed forest, deciduous forest, shrub and grass, bog, alpine tundra, barrens, snow and ice, and cultural features. Color coded maps and acreage summaries of the major land cover categories were generated for selected USGS quadrangles (1:250,000) which lie within the drainage basin. The project was completed within six months.
Morrissey, L. A.↗