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Results for “texture analysis”

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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Micromechanical Characterization and Texture Analysis of Direct Cast Titanium Alloys Strips

This research was conducted to determine a post-processing technique to optimize mechanical and material properties of a number of Titanium based alloys and aluminides processed via Melt Overflow Solidification Technique (MORST). This technique was developed by NASA for the development of thin sheet titanium and titanium aluminides used in high temperature applications. The materials investigated in this study included conventional titanium alloy strips and foils, Ti-1100, Ti-24Al-11Nb (Alpha-2), and Ti-48Al-2Ta (Gamma). The methodology used included micro-characterization, heat-treatment, mechanical processing and mechanical testing. Characterization techniques included optical, electron microscopy, and x-ray texture analysis. The processing included heat-treatment and mechanical deformation through cold rolling. The initial as-cast materials were evaluated for their microstructure and mechanical properties. Different heat-treatment and rolling steps were chosen to process these materials. The properties were evaluated further and a processing relationship was established in order to obtain an optimum processing condition. The results showed that the as-cast material exhibited a Widmanstatten (fine grain) microstructure that developed into a microstructure with larger grains through processing steps. The texture intensity showed little change for all processing performed in this investigation.

Source record↗
NASA NTRS1977

Textural analysis by statistical parameters and its application to the mapping of flow-structures in wetlands

From 1974 to 1977 the application of remote sensing methods in coastal areas and tidal bays and estuaries was investigated on the German coast of the North Sea. Aerial photographs were taken using different films; (1) color, (2) color infrared, and (3) black and white films. Scanner recordings were taken by an 11 channel scanner. Ground truth measurements of radiation and measurements of meteorological elements were carried out. For mapping the morphology in mudflat areas a digital texture analysis was developed, by which measurement of the change of image structures cased by distributing factors, such as changing illumination, is possible.

Wieczorek, U.↗
DOE OSTI2024

Virtual texture analysis to understand microstructure effects on deformation twinning and detwinning behavior in BCC metals

Understanding and predicting deformation twinning contributions to plastic deformation in BCC metals has been a long-standing challenge due to the interplay with dislocation slip and non-Schmid effects that render an asymmetry under tension and compression. This paper uses molecular dynamics simulations to understand the effect of unloading and grain orientation on deformation twinning in a nanocrystalline Fe (nc-Fe) system as a model BCC metal. The nc-Fe system is loaded under uniaxial stress tension and compression to understand the effect of grain orientation (Schmid effects) on deformation twinning behavior and the tension/compression asymmetry (non-Schmid effects). A new virtual texture analysis “VirTex” tool is used to understand the role of grain orientation on the nucleation of twins and their contributions to the observed stress–strain response. For certain grain orientations, the twinnability is observed to be different in tension and compression. In addition, the flow stress accommodation from twins in certain grains is observed to be different in tension and compression and different from that for the grains. Subsequent unloading leads to detwinning in the deformed microstructures, where the extent of detwinning depends on the strain from which the system is unloaded and on the morphology of the twin. Lastly, the simulations are carried out to analyze the role of the Schmid factor on the twinnability and asymmetry in tension and compression.

Kannan, Aadhithyan [University of Connecticut, Sto↗
NASA NTRS1989

Methods for the mineralogical and textural analysis of comet nucleus samples

The objectives and instrumental requirements of a petrographic analysis of porous comet nucleus material are reviewed. Assumptions about its composition and texture, and the available techniques for the microscopic analysis of comet analogue material are investigated. New techniques required for the petrographic investigation of natural and artificial comet nucleus samples are also considered.

Stoeffler, D.↗
NASA NTRS1983

Crop identification of SAR data using digital textural analysis

After preprocessing SEASAT SAR data which included slant to ground range transformation, registration to LANDSAT MSS data and appropriate filtering of the raw SAR data to minimize coherent speckle, textural features were developed based upon the spatial gray level dependence method (SGLDM) to compute entropy and inertia as textural measures. It is indicated that the consideration of texture features are very important in SAR data analysis. The SEASAT SAR data are useful for the improvement of field boundary definitions and for an earlier season estimate of corn and soybean area location than is supported by LANDSAT alone.

Nuesch, D. R.↗
DOE OSTI2025

Texture Analysis of Inconel 718 with Different Modes During Single-Track Laser Surface Re-Melting

An in-depth understanding of the texture formation in melt pools allows for the modification of the surface layer microstructure and corresponding material properties, providing an opportunity to integrate laser surface re-melting into metal additive manufacturing. This study investigates crystallographic texture formation at different cooling rates in single melting tracks on the Inconel 718 (IN718) plate produced by laser surface re-melting. The cooling rate varies from 2.31 × 10 5 °C/s to 9.56 × 10 5 °C/s with the increase in scanning rates from 400 mm/s to 1200 mm/s, measured by recently developed real-time temperature monitoring of melt pools. Columnar grains are dominant, with distinct crystallographic textures forming in the melt pools. At a slower scanning speed, the keyhole mode shows three different textures forming at different depths (crystallographically layered structure), while, at a faster scanning speed, the conduction mode shows only random grain orientation. There are no pores/voids detected, and the columnar grain morphology and columnar grain width (8.6 μm to 12.4 μm) follow the analysis framework in terms of thermal gradient and solidification rate analysis. This implies that laser surface re-melting provides the potential to modify the surface structure from a random grain orientation to a crystallographically layered structure.

36 MATERIALS SCIENCE↗
NASA NTRS1991

Texture analysis of radiometric signatures of new sea ice forming in Arctic leads

Analysis of 33.6-GHz, high-resolution, passive microwave images suggests that new sea ice accumulating in open leads is characterized by a unique textural signature which can be used to discriminate new ice forming in this environment from adjacent surfaces of similar radiometric temperature. Ten training areas were selected from the data set, three of which consisted entirely of first-year ice, four entirely of multilayer ice, and three of new ice in open leads in the process of freezing. A simple gradient operator was used to characterize the radiometric texture in each training region in terms of the degree to which radiometric gradients are oriented. New ice in leads has a sufficiently high proportion of well-oriented features to distinguish it uniquely from first-year ice and multiyear ice. The predominance of well-oriented features probably reflects physical processes by which new ice accumulates in open leads. Banded structures, which are evident in aerial photographs of new ice, apparently give rise to the radiometric signature observed, in which the trend of brightness temperature gradients is aligned parallel to lead trends. First-year ice and multiyear ice, which have been subjected to a more random growth and process history, lack this banded structure and therefore are characterized by signatures in which well-aligned elements are less dominant.

Eppler, Duane T.↗
NASA NTRS2010

Instrument and method for X-ray diffraction, fluorescence, and crystal texture analysis without sample preparation

An X-ray diffraction and X-ray fluorescence instrument for analyzing samples having no sample preparation includes a X-ray source configured to output a collimated X-ray beam comprising a continuum spectrum of X-rays to a predetermined coordinate and a photon-counting X-ray imaging spectrometer disposed to receive X-rays output from an unprepared sample disposed at the predetermined coordinate upon exposure of the unprepared sample to the collimated X-ray beam. The X-ray source and the photon-counting X-ray imaging spectrometer are arranged in a reflection geometry relative to the predetermined coordinate.

Gendreau, Keith↗
NASA NTRS1982

A Study of Feature Extraction Using Divergence Analysis of Texture Features

An empirical study of texture analysis for feature extraction and classification of high spatial resolution remotely sensed imagery (10 meters) is presented in terms of specific land cover types. The principal method examined is the use of spatial gray tone dependence (SGTD). The SGTD method reduces the gray levels within a moving window into a two-dimensional spatial gray tone dependence matrix which can be interpreted as a probability matrix of gray tone pairs. Haralick et al (1973) used a number of information theory measures to extract texture features from these matrices, including angular second moment (inertia), correlation, entropy, homogeneity, and energy. The derivation of the SGTD matrix is a function of: (1) the number of gray tones in an image; (2) the angle along which the frequency of SGTD is calculated; (3) the size of the moving window; and (4) the distance between gray tone pairs. The first three parameters were varied and tested on a 10 meter resolution panchromatic image of Maryville, Tennessee using the five SGTD measures. A transformed divergence measure was used to determine the statistical separability between four land cover categories forest, new residential, old residential, and industrial for each variation in texture parameters.

Hallada, W. A.↗
NASA NTRS1981

Textural features for radar image analysis

Texture is seen as an important spatial feature useful for identifying objects or regions of interest in an image. While textural features have been widely used in analyzing a variety of photographic images, they have not been used in processing radar images. A procedure for extracting a set of textural features for characterizing small areas in radar images is presented, and it is shown that these features can be used in classifying segments of radar images corresponding to different geological formations.

Shanmugan, K. S.↗
NASA NTRS2011

Fast Image Texture Classification Using Decision Trees

Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.

Thompson, David R.↗
NASA NTRS2000

Independent Component Analysis of Textures

A common method for texture representation is to use the marginal probability densities over the outputs of a set of multi-orientation, multi-scale filters as a description of the texture. We propose a technique, based on Independent Components Analysis, for choosing the set of filters that yield the most informative marginals, meaning that the product over the marginals most closely approximates the joint probability density function of the filter outputs. The algorithm is implemented using a steerable filter space. Experiments involving both texture classification and synthesis show that compared to Principal Components Analysis, ICA provides superior performance for modeling of natural and synthetic textures.

Manduchi, Roberto↗
NASA NTRS1983

Image processing in remote sensing data analysis - The state of the art

Image analysis techniques applicable to remote sensing data and covering image models, feature detection, segmentation and classification, texture analysis, and matching are studied. Model types for characterizing images examined include random-field, mosaic, and facet models. Edge and corner detection as well as global extraction of linear features are discussed. Pixel clustering and classification are covered in addition to the regional approach to segmentation. Autocorrelation, second-order gray level probability density, and the use of primitive element statistics are discussed in relation to texture analysis. Finally, reducing the cost of (sub)imaging matching methods (e.g., pixelwise comparison of gray levels and normalized cross-correlation between two images) as well as improving match sharpness is considered.

Rosenfeld, A.↗
NASA NTRS1972

An Analysis of Texture on Lunar Ground Photos

An experiment was performed in an attempt to evaluate autocorrelation as an indicator of texture using five small patches representing varying distances on each of four lunar photos. Each patch was scanned at 50 micron increments on a 64-level gray scale. Several problems were involved in using the ordinary autocorrelation value but a rougher autocorrelation measure, the percentage of neighboring pairs (at a given lag distance) which fell within one graylevel of one another, yielded very encouraging results. As the distance to the feature decreased, initial slope of the graph of percentage against lag increased, and the general level of the graph decreased. When a crestline was present, the graph tended to continue to decrease at higher lags as well, rather than leveling off after the initial steep slope.

Olson, J. M.↗