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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

SEASAT views oceans and sea ice with synthetic aperture radar

Fifty-one SEASAT synthetic aperture radar (SAR) images of the oceans and sea ice are presented. Surface and internal waves, the Gulf Stream system and its rings and eddies, the eastern North Pacific, coastal phenomena, bathymetric features, atmospheric phenomena, and ship wakes are represented. Images of arctic pack and shore-fast ice are presented. The characteristics of the SEASAT SAR system and its image are described. Maps showing the area covered, and tables of key orbital information, and listing digitally processed images are provided.

Fu, L. L.↗
NASA NTRS1986

Optimum retrieval of precipitable water fields from VAS and surface data

The use of VAS radiances and conventional surface data to determine the optimum resolution and accuracy of low-level precipitable water fields retrieved from geosynchronous satellite observations is examined. VAS retrievals of precipitated water (PW) are compared with various channel selections in the regression algorithm and with the split window algorithm. The split window algorithm and the linear regression algorithm are described. Consideration is given to the sounding-field-of-view (SFOV) resolution; the data reveal that the optimum resolution for the split window algorithm is at 15-60 km resolution, and 30-60 km SFOV resolution is optimal for the regression method. Statistics and images from PW retrieval experiments conducted on July 13, 1981 are presented and utilized to determine optimum channel selection. A fast contouring method for VAS sounding images is proposed.

Chesters, Dennis↗
NASA NTRS2009

Flightspeed Integral Image Analysis Toolkit

The Flightspeed Integral Image Analysis Toolkit (FIIAT) is a C library that provides image analysis functions in a single, portable package. It provides basic low-level filtering, texture analysis, and subwindow descriptor for applications dealing with image interpretation and object recognition. Designed with spaceflight in mind, it addresses: Ease of integration (minimal external dependencies) Fast, real-time operation using integer arithmetic where possible (useful for platforms lacking a dedicated floatingpoint processor) Written entirely in C (easily modified) Mostly static memory allocation 8-bit image data The basic goal of the FIIAT library is to compute meaningful numerical descriptors for images or rectangular image regions. These n-vectors can then be used directly for novelty detection or pattern recognition, or as a feature space for higher-level pattern recognition tasks. The library provides routines for leveraging training data to derive descriptors that are most useful for a specific data set. Its runtime algorithms exploit a structure known as the "integral image." This is a caching method that permits fast summation of values within rectangular regions of an image. This integral frame facilitates a wide range of fast image-processing functions. This toolkit has applicability to a wide range of autonomous image analysis tasks in the space-flight domain, including novelty detection, object and scene classification, target detection for autonomous instrument placement, and science analysis of geomorphology. It makes real-time texture and pattern recognition possible for platforms with severe computational restraints. The software provides an order of magnitude speed increase over alternative software libraries currently in use by the research community. FIIAT can commercially support intelligent video cameras used in intelligent surveillance. It is also useful for object recognition by robots or other autonomous vehicles

Thompson, David R.↗
NASA NTRS2008

Algorithm for Wavefront Sensing Using an Extended Scene

A recently conceived algorithm for processing image data acquired by a Shack-Hartmann (SH) wavefront sensor is not subject to the restriction, previously applicable in SH wavefront sensing, that the image be formed from a distant star or other equivalent of a point light source. That is to say, the image could be of an extended scene. (One still has the option of using a point source.) The algorithm can be implemented in commercially available software on ordinary computers. The steps of the algorithm are the following: 1. Suppose that the image comprises M sub-images. Determine the x,y Cartesian coordinates of the centers of these sub-images and store them in a 2xM matrix. 2. Within each sub-image, choose an NxN-pixel cell centered at the coordinates determined in step 1. For the ith sub-image, let this cell be denoted as si(x,y). Let the cell of another subimage (preferably near the center of the whole extended-scene image) be designated a reference cell, denoted r(x,y). 3. Calculate the fast Fourier transforms of the sub-sub-images in the central NxN portions (where N < N and both are preferably powers of 2) of r(x,y) and si(x,y). 4. Multiply the two transforms to obtain a cross-correlation function Ci(u,v), in the Fourier domain. Then let the phase of Ci(u, v) constitute a phase function, phi(u,v). 5. Fit u and v slopes to phi (u,v) over a small u,v subdomain. 6. Compute the fast Fourier transform, Si(u,v) of the full NxN cell si(x,y). Multiply this transform by the u and phase slopes obtained in step 4. Then compute the inverse fast Fourier transform of the product. 7. Repeat steps 4 through 6 in an iteration loop, cumulating the u and slopes, until a maximum iteration number is reached or the change in image shift becomes smaller than a predetermined tolerance. 8. Repeat steps 4 through 7 for the cells of all other sub-images.

Sidick, Erkin↗
NASA NTRS1973

Image processing techniques and applications to the Earth Resources Technology Satellite program

The Earth Resources Technology Satellite system is studied, with emphasis on sensors, data processing requirements, and image data compression using the Fast Fourier and Hadamard transforms. The ERTS-A system and the fundamentals of remote sensing are discussed. Three user applications (forestry, crops, and rangelands) are selected and their spectral signatures are described. It is shown that additional sensors are needed for rangeland management. An on-board information processing system is recommended to reduce the amount of data transmitted.

Polge, R. J.↗
NASA NTRS1984

Computer vision for real-time orbital operations. Center directors discretionary fund

Machine vision research is examined as it relates to the NASA Space Station program and its associated Orbital Maneuvering Vehicle (OMV). Initial operation of OMV for orbital assembly, docking, and servicing are manually controlled from the ground by means of an on board TV camera. These orbital operations may be accomplished autonomously by machine vision techniques which use the TV camera as a sensing device. Classical machine vision techniques are described. An alternate method is developed and described which employs a syntactic pattern recognition scheme. It has the potential for substantial reduction of computing and data storage requirements in comparison to the Two-Dimensional Fast Fourier Transform (2D FFT) image analysis. The method embodies powerful heuristic pattern recognition capability by identifying image shapes such as elongation, symmetry, number of appendages, and the relative length of appendages.

Vinz, F. L.↗
NASA NTRS1985

The great flare of 1982 June 6

The great soft X-ray (SXR) flare (X12) of the past solar maximum was observed by Hinotori and by Big Bear Solar Observatory (BBSO) on June 6, 1982. Hinotori data consist of hard X-ray (HXR) and SXR images in the rise and decay of the flare, high-resolution soft X-ray spectra throughout the flare, and HXR and gamma-ray data. The BBSO data include films of H-alpha, H-alpha blue wing, D3 and longitudinal magnetic field, as well as video tapes of continuum. Images in HXR, SXR, H-alpha, D3 and the continuum are compared and SXR spectra analyzed. The flare resulted from extended motion of a large spot shearing the magnetic field. D3 and white-light images exhibit a progression from fast flashes to two ribbons, while both HXR and SXR are centered on the optical kernels. The continuum emission shows the same temporal behavior as the HXR at 160 keV. In its early phases, the Fe XXV line was double-peaked, and a decreasing blueshifted (up to 400 km/sec) component was observed, from which the evaporation rate of chromospheric material was estimated. It is suggested that this upflow is adequate to supply the coronal cloud. Flare energetics are discussed in detail, and it is concluded that a significant amount of energy was deposited in the corona, and that nonthermal electrons are the major energy input.

Tanaka, K.↗
NASA NTRS1992

Reduction of blocking effects for the JPEG baseline image compression standard

Transform coding has been chosen for still image compression in the Joint Photographic Experts Group (JPEG) standard. Although transform coding performs superior to many other image compression methods and has fast algorithms for implementation, it is limited by a blocking effect at low bit rates. The blocking effect is inherent in all nonoverlapping transforms. This paper presents a technique for reducing blocking while remaining compatible with the JPEG standard. Simulations show that the system results in subjective performance improvements, sacrificing only a marginal increase in bit rate.

Zweigle, Gregary C.↗
NASA NTRS2001

Radarsat Antarctic Mapping Project: Antarctic Imaging Campaign 2

The Radarsat Antarctic Mapping Project is a collaboration between NASA and the Canadian Space Agency to map Antarctica using synthetic aperture radar (SAR). The first Antarctic Mapping Mission (AMM-1) was successfully completed in October 1997. Data from the acquisition phase of the 1997 campaign have been used to achieve the primary goal of producing the first, high-resolution SAR image map of Antarctica. The limited amount of data suitable for interferometric analysis have also been used to produce remarkably detailed maps of surface velocity for a few selected regions. Most importantly, the results from AMM-1 are now available to the general science community in the form of various resolution, radiometrically calibrated and geometrically accurate image mosaics. The second Antarctic imaging campaign occurred during the fall of 2000. Modified from AMM-1, the satellite remained in north looking mode during AMM-2 restricting coverage to regions north of about -80 degrees latitude. But AMM-2 utilized for the first time RADARSAT-1 fine beams providing an unprecedented opportunity to image many of Antarctica's fast glaciers whose extent was revealed through AMM-1 data. AMM-2 also captured extensive data suitable for interferometric analysis of the surface velocity field. This report summarizes the science goals, mission objectives, and project status through the acquisition phase and the start of the processing phase. The reports describes the efforts of team members including Alaska SAR Facility, Jet Propulsion Laboratory, Vexcel Corporation, Goddard Space Flight Center, Wallops Flight Facility, Ohio State University, Environmental Research Institute of Michigan, White Sands Facility, Canadian Space Agency Mission Planning and Operations Groups, and the Antarctic Mapping Planning Group.

Source record↗
NASA NTRS2009

Low-Complexity Lossless and Near-Lossless Data Compression Technique for Multispectral Imagery

This work extends the lossless data compression technique described in Fast Lossless Compression of Multispectral- Image Data, (NPO-42517) NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26. The original technique was extended to include a near-lossless compression option, allowing substantially smaller compressed file sizes when a small amount of distortion can be tolerated. Near-lossless compression is obtained by including a quantization step prior to encoding of prediction residuals. The original technique uses lossless predictive compression and is designed for use on multispectral imagery. A lossless predictive data compression algorithm compresses a digitized signal one sample at a time as follows: First, a sample value is predicted from previously encoded samples. The difference between the actual sample value and the prediction is called the prediction residual. The prediction residual is encoded into the compressed file. The decompressor can form the same predicted sample and can decode the prediction residual from the compressed file, and so can reconstruct the original sample. A lossless predictive compression algorithm can generally be converted to a near-lossless compression algorithm by quantizing the prediction residuals prior to encoding them. In this case, since the reconstructed sample values will not be identical to the original sample values, the encoder must determine the values that will be reconstructed and use these values for predicting later sample values. The technique described here uses this method, starting with the original technique, to allow near-lossless compression. The extension to allow near-lossless compression adds the ability to achieve much more compression when small amounts of distortion are tolerable, while retaining the low complexity and good overall compression effectiveness of the original algorithm.

Xie, Hua↗
NASA NTRS2004

Compact Microscope Imaging System with Intelligent Controls

The figure presents selected views of a compact microscope imaging system (CMIS) that includes a miniature video microscope, a Cartesian robot (a computer- controlled three-dimensional translation stage), and machine-vision and control subsystems. The CMIS was built from commercial off-the-shelf instrumentation, computer hardware and software, and custom machine-vision software. The machine-vision and control subsystems include adaptive neural networks that afford a measure of artificial intelligence. The CMIS can perform several automated tasks with accuracy and repeatability . tasks that, heretofore, have required the full attention of human technicians using relatively bulky conventional microscopes. In addition, the automation and control capabilities of the system inherently include a capability for remote control. Unlike human technicians, the CMIS is not at risk of becoming fatigued or distracted: theoretically, it can perform continuously at the level of the best human technicians. In its capabilities for remote control and for relieving human technicians of tedious routine tasks, the CMIS is expected to be especially useful in biomedical research, materials science, inspection of parts on industrial production lines, and space science. The CMIS can automatically focus on and scan a microscope sample, find areas of interest, record the resulting images, and analyze images from multiple samples simultaneously. Automatic focusing is an iterative process: The translation stage is used to move the microscope along its optical axis in a succession of coarse, medium, and fine steps. A fast Fourier transform (FFT) of the image is computed at each step, and the FFT is analyzed for its spatial-frequency content. The microscope position that results in the greatest dispersal of FFT content toward high spatial frequencies (indicating that the image shows the greatest amount of detail) is deemed to be the focal position.

McDowell, Mark↗
NASA NTRS2003

Use of Multi-Resolution Wavelet Feature Pyramids for Automatic Registration of Multi-Sensor Imagery

The problem of image registration, or alignment of two or more images representing the same scene or object, has to be addressed in various disciplines that employ digital imaging. In the area of remote sensing, just like in medical imaging or computer vision, it is necessary to design robust, fast and widely applicable algorithms that would allow automatic registration of images generated by various imaging platforms at the same or different times, and that would provide sub-pixel accuracy. One of the main issues that needs to be addressed when developing a registration algorithm is what type of information should be extracted from the images being registered, to be used in the search for the geometric transformation that best aligns them. The main objective of this paper is to evaluate several wavelet pyramids that may be used both for invariant feature extraction and for representing images at multiple spatial resolutions to accelerate registration. We find that the band-pass wavelets obtained from the Steerable Pyramid due to Simoncelli perform better than two types of low-pass pyramids when the images being registered have relatively small amount of nonlinear radiometric variations between them. Based on these findings, we propose a modification of a gradient-based registration algorithm that has recently been developed for medical data. We test the modified algorithm on several sets of real and synthetic satellite imagery.

Zavorin, Ilya↗
NASA NTRS2003

Distal airways in humans: dynamic hyperpolarized 3He MR imaging--feasibility

Dynamic hyperpolarized helium 3 (3He) magnetic resonance (MR) imaging of the human airways is achieved by using a fast gradient-echo pulse sequence during inhalation. The resulting dynamic images show differential contrast enhancement of both distal airways and the lung periphery, unlike static hyperpolarized 3He MR images on which only the lung periphery is seen. With this technique, up to seventh-generation airway branching can be visualized. Copyright RSNA, 2003.

NASA Discipline Life Sciences Technologies↗
NASA NTRS2005

Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery

The problem of image registration, or the alignment of two or more images representing the same scene or object, has to be addressed in various disciplines that employ digital imaging. In the area of remote sensing, just like in medical imaging or computer vision, it is necessary to design robust, fast, and widely applicable algorithms that would allow automatic registration of images generated by various imaging platforms at the same or different times and that would provide subpixel accuracy. One of the main issues that needs to be addressed when developing a registration algorithm is what type of information should be extracted from the images being registered, to be used in the search for the geometric transformation that best aligns them. The main objective of this paper is to evaluate several wavelet pyramids that may be used both for invariant feature extraction and for representing images at multiple spatial resolutions to accelerate registration. We find that the bandpass wavelets obtained from the steerable pyramid due to Simoncelli performs best in terms of accuracy and consistency, while the low-pass wavelets obtained from the same pyramid give the best results in terms of the radius of convergence. Based on these findings, we propose a modification of a gradient-based registration algorithm that has recently been developed for medical data. We test the modified algorithm on several sets of real and synthetic satellite imagery.

Evaluation Studies↗
NASA NTRS1987

The Penn State astronomical image processing system

The needs of modern astronomy for image processing set demanding standards in simultaneously requiring fast computation speed, high-quality graphic display, large data storage, and interactive response. An innovative image processing system was designed, integrated, and used; it is based on a supermicro architecture which is tailored specifically for astronomy, which provides a highly cost-effective alternative to the traditional minicomputer installation. The paper describes the design rationale, equipment selection, and software developed to allow other astronomers with similar needs to benefit from the present experience.

Truax, Ryland J.↗
NASA NTRS1991

The Mees CCD imaging spectrograph

The Mees CCD (MCCD0 instrument is an imaging spectroscopy device which uses the 25-cm coronagraph telescope and the 3.0-m Coude spectrograph at Mees Solar Observatory on Haleakala, Maui. The instrument works with resolving power up to about 200,000 with significant throughput from 3934 A (Ca II K) to about 10,000 A. A fast guiding active mirror stabilizes the image during observations. A rapidly writing magnetic tape storage system allows observations to be recorded at 256 kbytes/s. Currently, the MCCD is used for imaging spectroscopy of solar flares at 6563 A (H-alpha), and velocity measurements of umbral oscillations; future plans include emission line studies of active region coronae, and photospheric studies of solar oscillations.

Penn, Matthew J.↗
NASA NTRS2012

Fast and Adaptive Lossless Onboard Hyperspectral Data Compression System

Modern hyperspectral imaging systems are able to acquire far more data than can be downlinked from a spacecraft. Onboard data compression helps to alleviate this problem, but requires a system capable of power efficiency and high throughput. Software solutions have limited throughput performance and are power-hungry. Dedicated hardware solutions can provide both high throughput and power efficiency, while taking the load off of the main processor. Thus a hardware compression system was developed. The implementation uses a field-programmable gate array (FPGA). The implementation is based on the fast lossless (FL) compression algorithm reported in Fast Lossless Compression of Multispectral-Image Data (NPO-42517), NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26, which achieves excellent compression performance and has low complexity. This algorithm performs predictive compression using an adaptive filtering method, and uses adaptive Golomb coding. The implementation also packetizes the coded data. The FL algorithm is well suited for implementation in hardware. In the FPGA implementation, one sample is compressed every clock cycle, which makes for a fast and practical realtime solution for space applications. Benefits of this implementation are: 1) The underlying algorithm achieves a combination of low complexity and compression effectiveness that exceeds that of techniques currently in use. 2) The algorithm requires no training data or other specific information about the nature of the spectral bands for a fixed instrument dynamic range. 3) Hardware acceleration provides a throughput improvement of 10 to 100 times vs. the software implementation. A prototype of the compressor is available in software, but it runs at a speed that does not meet spacecraft requirements. The hardware implementation targets the Xilinx Virtex IV FPGAs, and makes the use of this compressor practical for Earth satellites as well as beyond-Earth missions with hyperspectral instruments.

Aranki, Nazeeh I.↗