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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 NTRS1992

Registering and resampling images in STSDAS

Registering different images can be difficult, especially if the images to be registered are images at different wavelengths, where features in one image may look entirely different or be absent from the second image. Using two new packages soon to be added to the STSDAS package, REGISTER and RESAMPLE, this job is done automatically. The REGISTER package allows the user to determine the amount of translation, rotation, and/or magnification needed to make two images, spectra, or time series congruent. The methods implemented to compute the registration parameters use: a set of the pixel coordinates of the same features identified in two files, or the FITS coordinate transformation parameters in the headers of two data files, or a single feature identified as the peak of a cross-correlation between two vectors. The coefficients describing the registration are defined by the equations (for a two-dimensional image): x = a + bx + cy, y = d + ex + fy, where (X,Y) are the pixel coordinates of a feature in the reference image, (x,y) are the pixel coordinates of a feature in the secondary image, and the computed coefficients are a, b, c, d, e, and f. Results may be produced by linking the output of REGISTER to RESAMPLE in a command language procedure. The output from REGISTER and the input to RESAMPLE consists of a matrix of coefficients (a through f above) fully specifying the registration. The RESAMPLE package resamples simple vector or image data for a given amount of translation, rotation (images only), and magnification, or reflection of the science data. Specific options included are: image rotation about the FITS reference pixel; scale changes, i.e. magnification or demagnification (for images, independently on both axes); simple translation; reflection (for images, about one or both axes); and resampling and registration to a reference data set. Output from the RESAMPLE task is the resampled image which may then be displayed and compared with the reference image.

Williamson, R. L., II↗
NASA NTRS1982

Report of the subpanel on resampling functions

The state of the art and anticipated requirements for resampling in applications of remote sensing are examined. It is noted that the wide range of potential applications and expanded needs for resampling versatility in new imaging sensors leads to a concern for creating standardized test data sets and ground training sites. Several research areas are defined including the development of (1) a theory for defining an input psf of an instrument such that upon resampling of the processed data into the desired output grid the radiometric and geometric properties are a best estimate of the true upwelling radiances; (2) algorithms for estimating missing data or for ignoring missing data; (3) optimal resampling functions; (4) standard data sets; and (5) merit functions for assessing resampling techniques.

Source record↗
NASA NTRS1982

A quantitative assessment of resampling errors

Applications associated with digital geographic imagery are subject to great diversity in required cell size, cartographic projection, etc. The need for resampling remote sensing scaner data is evident in all but the most undemanding cases. It is shown that proper resampling of such data is dependent in important ways on the detailed knowledge of the original scanner's effective point-spread function and to the desired point-spread function of resampled data. When both of these are known, it is relatively straightforward to compute the resampling coefficients which do the best job of approximating the shape and position of the synthesized point-spread function. The resulting synthesized psf are compared with an ideal psf located at various interpixel positions and any differences observed as errors.

Dye, R. H.↗
NASA NTRS1981

Landsat D Thematic Mapper image resampling for scan geometry correction

The Landsat D system is described, and the geometric correction processing for the Thematic Mapper (TM) is reviewed. The resampling procedure (the generation of a TM output image) is analyzed with emphasis on the effect of sampling geometry on output image. Effects of scan gaps and spacecraft jitter on output image are studied by means of a simulation of the sampling and resampling processes for three sampling geometries. Visual inspection of the resampling results shows that the resampling algorithm works excellently under all conditions and that distortion is visible only in the rare case of large gaps between scans.

Prakash, A.↗
NASA NTRS1997

An optical systems analysis approach to image resampling

All types of image registration require some type of resampling, either during the registration or as a final step in the registration process. Thus the image(s) must be regridded into a spatially uniform, or angularly uniform, coordinate system with some pre-defined resolution. Frequently the ending resolution is not the resolution at which the data was observed with. The registration algorithm designer and end product user are presented with a multitude of possible resampling methods each of which modify the spatial frequency content of the data in some way. The purpose of this paper is threefold: (1) to show how an imaging system modifies the scene from an end to end optical systems analysis approach, (2) to develop a generalized resampling model, and (3) empirically apply the model to simulated radiometric scene data and tabulate the results. A Hanning windowed sinc interpolator method will be developed based upon the optical characterization of the system. It will be discussed in terms of the effects and limitations of sampling, aliasing, spectral leakage, and computational complexity. Simulated radiometric scene data will be used to demonstrate each of the algorithms. A high resolution scene will be "grown" using a fractal growth algorithm based on mid-point recursion techniques. The result scene data will be convolved with a point spread function representing the optical response. The resultant scene will be convolved with the detection systems response and subsampled to the desired resolution. The resultant data product will be subsequently resampled to the correct grid using the Hanning windowed sinc interpolator and the results and errors tabulated and discussed.

Lyon, Richard G.↗
NASA NTRS1977

Resampling study

The author has identified the following significant results. The nearest neighbor and cubic convolution resampling algorithms were applied to a variety of images extracted from LANDSAT MSS data. A comparison of the results demonstrated that (1) cubic convolution can cause spreading of small features and can introduce noticeable overshoot (ringing) into the data; (2) cubic convolution attenuates the high spatial frequencies compared to the original and nearest neighbor resampled data; and (3) cubic convolution generally produces photographic products of superior visual quality. The effects of the resampling algorithms on multispectral classification were not conclusively determined due to the small number of images tested.

Ferneyhough, D. G.↗
NASA NTRS1984

An optimized cubic interpolator for image resampling

It is noted that the cubic resampling function is only one member of a family of functions, defined by the single parameter of the slope of the cubic function at its first zero crossing, whose other members are in some cases superior to the standard cubic. This superiority is especially noteworthy with respect to the extent of gray level overshoot induced by the resampling process at high contrast edges. It is shown that there is an optimum member of this 'parametric cubic convolution' family which minimizes the mean-squared radiometric error arising from interpolation. This interpolator requires no additional computation time over the conventional cubic one. These conclusions are supported and illustrated by resampling simulations with both a high resolution digitized aerial image and a Landsat Multispectral Scanner image.

Schowengerdt, R. A.↗
NASA NTRS1977

Bi-resampled data study

The results of an experimental study conducted to determine the geometric and radiometric effects of double resampling (bi-resampling) performed on image data in the process of performing map projection transformations are reported.

Benner, R.↗
NASA NTRSDate not supplied

Derivation of Integrated Load Distributions from Resampled Computational Data

Principal component analysis (PCA) has been the center of many surrogate models used to characterize fluid flows in recent years. However, little work has been done to character- ize the uncertainty in the PCA transform itself and its effect on derived surrogate models. To explore the uncertainty, a typical interpolated surrogate model is constructed for a represen- tative aerodynamic body from computational data. The computational data is then resampled to explore the robustness of the PCA transformation. The variations of the model predictions during this resampling are analyzed to get a measure of confidence in the PCA transformation, which is then applied to the interpolated surrogate model to get uncertainty on integrated force predictions. An initial test case has been explored with promising results.

Principal Component Analysis↗
NASA NTRSDate not supplied

Derivation of Integrated Load Distributions from Resampled Computational Data

Principal component analysis (PCA) has been the center of many surrogate models used to characterize fluid flows in recent years. However, little work has been done to character- ize the uncertainty in the PCA transform itself and its effect on derived surrogate models. To explore the uncertainty, a typical interpolated surrogate model is constructed for a represen- tative aerodynamic body from computational data. The computational data is then resampled to explore the robustness of the PCA transformation. The variations of the model predictions during this resampling are analyzed to get a measure of confidence in the PCA transformation, which is then applied to the interpolated surrogate model to get uncertainty on integrated force predictions. An initial test case has been explored with promising results.

Computational Fluid Dynamics↗
NASA NTRS1985

Modified cubic convolution resampling for Landsat

An overview is given of Landsat Thematic Mapper resampling technique, including a modification of the well-known cubic convolution interpolator (nearest neighbor interpolation) used to provide geometric correction for TM data. Post launch study has shown that the modified cubic convolution interpolator can selectively enhance or suppress frequency bands in the output image. This selectivity is demonstrated on TM Band 3 imagery.

Prakash, A.↗
NASA NTRS1989

Ideal Resampling Of Discrete Sequences

Spectral information preserved to extent possible. Technique developed to shrink or expand discrete input sequence of numbers into output sequence in manner preserving input spectrum up to Nyquist limit of smaller of two sequences. In case of expansion, technique also prevents introduction of spurious frequencies not present in input. While applicable to many kinds of data sampled at regular interval, particularly useful in processing and enhancement of images, where discrete sequences spatially ordered sets of picture-element brightness values. Image coarsened by resampling to reduce number of picture elements while preserving as much as possible of original image information. Used to generate intermediate images at small intervals between frames, thereby suppressing appearance of jerky motion caused by sudden jumps between frames at low frame rate.

Watson, Andrew B.↗
NASA NTRS2009

Communication Optimizations for a Wireless Distributed Prognostic Framework

Distributed architecture for prognostics is an essential step in prognostic research in order to enable feasible real-time system health management. Communication overhead is an important design problem for such systems. In this paper we focus on communication issues faced in the distributed implementation of an important class of algorithms for prognostics - particle filters. In spite of being computation and memory intensive, particle filters lend well to distributed implementation except for one significant step - resampling. We propose new resampling scheme called parameterized resampling that attempts to reduce communication between collaborating nodes in a distributed wireless sensor network. Analysis and comparison with relevant resampling schemes is also presented. A battery health management system is used as a target application. A new resampling scheme for distributed implementation of particle filters has been discussed in this paper. Analysis and comparison of this new scheme with existing resampling schemes in the context for minimizing communication overhead have also been discussed. Our proposed new resampling scheme performs significantly better compared to other schemes by attempting to reduce both the communication message length as well as number total communication messages exchanged while not compromising prediction accuracy and precision. Future work will explore the effects of the new resampling scheme in the overall computational performance of the whole system as well as full implementation of the new schemes on the Sun SPOT devices. Exploring different network architectures for efficient communication is an importance future research direction as well.

Saha, Sankalita↗
NASA NTRS1984

Simulation of electronic registration of multispectral remote sensing images to 0.1 pixel accuracy

Band-to-band coregistration of multispectral remote sensing images can be achieved by electronic signal processing techniques rather than by costly and difficult mechanical alignment. This paper describes the results of a study of the end-to-end performance of electronic registration. The software simulation includes steps which model the performance of the geometric calibration process, the instrument image quality, detector performance and the effects of achieving coregistration through image resampling. The image resampling step emulates the Pipelined Resampling Processor, a real-time image resampler. The study demonstrates that the electronic alignment technique produces multispectral images which are superior to those produced by an imager whose pixel geometry is accurate to 0.1 pixel rms. The implications of this approach for future earth observation programs are discussed.

Reitsema, H. J.↗
NASA NTRS1976

Experimental study of digital image processing techniques for LANDSAT data

The author has identified the following significant results. Results are reported for: (1) subscene registration, (2) full scene rectification and registration, (3) resampling techniques, (4) and ground control point (GCP) extraction. Subscenes (354 pixels x 234 lines) were registered to approximately 1/4 pixel accuracy and evaluated by change detection imagery for three cases: (1) bulk data registration, (2) precision correction of a reference subscene using GCP data, and (3) independently precision processed subscenes. Full scene rectification and registration results were evaluated by using a correlation technique to measure registration errors of 0.3 pixel rms thoughout the full scene. Resampling evaluations of nearest neighbor and TRW cubic convolution processed data included change detection imagery and feature classification. Resampled data were also evaluated for an MSS scene containing specular solar reflections.

Rifman, S. S.↗