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NASA NTRS1991

Medical Imaging System

The MD Image System, a true-color image processing system that serves as a diagnostic aid and tool for storage and distribution of images, was developed by Medical Image Management Systems, Huntsville, AL, as a "spinoff from a spinoff." The original spinoff, Geostar 8800, developed by Crystal Image Technologies, Huntsville, incorporates advanced UNIX versions of ELAS (developed by NASA's Earth Resources Laboratory for analysis of Landsat images) for general purpose image processing. The MD Image System is an application of this technology to a medical system that aids in the diagnosis of cancer, and can accept, store and analyze images from other sources such as Magnetic Resonance Imaging.

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NASA NTRS2007

An Attempt to Observe Debris from the Breakup of a Titan 3C-4 Transtage

In February 2007 dedicated observations were made of the orbital space predicted to contain debris from the breakup of the Titan 3C-4 transtage back on February 21, 1992. These observations were carried out on the Michigan Orbital DEbris Survey Telescope (MODEST) in Chile with its 1.3deg field of view. The search region or orbital space (inclination and right ascension of the ascending node (RAAN) was predicted using NASA#s LEGEND (LEO-to-GEO Environment Debris) code to generate a Titan debris cloud. Breakup fragments are created based on the NASA Standard Breakup Model (including fragment size, area-to-mass (A/M), and delta-V distributions). Once fragments are created, they are propagated forward in time with a subroutine GEOPROP. Perturbations included in GEOPROP are those due to solar/lunar gravity, radiation pressure, and major geopotential terms. Barker, et. al, (AMOS Conference Proceedings, 2006, pp. 596-604) used similar LEGEND predictions to correlate survey observations made by MODEST (February 2002) and found several possible night-to-night correlations in the limited survey dataset. One conc lusion of the survey search was to dedicate a MODEST run to observing a GEO region predicted to contain debris fragments and actual Titan debris objects (SSN 25000, 25001 and 30000). Such a dedicated run was undertaken with MODEST between February 17 and 23, 2007 (UT dates). MODEST#s limiting magnitude of 18.0 (S\N approx.10) corresponds to a size of 22cm assuming a diffuse Lambertian albedo of 0.2. However, based on observed break-up data, we expect most debris fragments to be smaller than 22cm which implies a need to increase the effective sensitivity of MODEST for smaller objects. MODEST#s limiting size can be lowered by increasing the exposure time (20 instead of 5 seconds) and applying special image processing. The special processing combines individual CCD images to detect faint objects that are invisible on a single CCD image. Sub-images are cropped from six consecutive CCD images with pixel shifts between images being consistent with the predicted movement of a Titan object. A median image of all the sub-images is then created leaving only those objects with the proper Titan motion. Limiting the median image in this manner brings the needed computer time to process all images taken on one night down to about 50 hours of CPU time.

Barker, E. S.↗

NASA NTRS1985

1985 International Geoscience and Remote Sensing Symposium (IGARSS '85), University of Massachusetts, Amherst, October 7-9, 1985, Digest. Volumes 1 & 2

The present conference on remote sensing instrumentation considers topics in water resources research, planetary remote sensing and mathematical geophysics, sea ice behavior in view of the Cold Regions Research and Engineering Laboratories pond measurements, Shuttle Imaging Radar-B (SIR-B) system performance and calibration results, geological applications of remote sensing, and microwave scattering from vegetation. Further attention is given to atmospheric probing with lasers, image processing for remote sensing, forest inventory and condition assessment, atmospheric remote sensing, SIR-B geological results, the reflectance, emission, and scattering characteristics of vegetation, sea ice sensing, multipolarization SAR results, the theoretical modeling of surfaces and volumes, crop condition assessment and productivity estimates, and SIR-B results for vegetation cover. Also considered are the microwave remote sensing of soil moisture, advanced sensors, advanced information extraction, wind and wave remote observations, results from the ESA Remote Sensing satellite, large area crop and land cover statistics, SIR-B results for directional ocean wave spectra, seasonal snow cover, ice sheets, and lake ice, fundamental research in terrain remote sensing, image classification methods, vegetation stress detection, remote sensing of coastal processes, SAR systems and calibration, electromagnetic geophysical methods and signal processing, image processing techniques, and SAR internal wave measurements.

Carver, K. R.↗

NASA NTRS1986

Nature and origin of mineral coatings on volcanic rocks of the Black Mountain, Stonewall Mountain and Kane Springs Wash volcanic centers, southern Nevada

Surface coatings on volcanic rock assemblages that occur at select tertiary volcanic centers in southern Nevada were investigated using LANDSAT 5 Thematic Mapper imagery. Three project sites comprise the subject of this study: the Kane Springs Wash, Black Mountain, and Stonewall Mountain volcanic centers. LANDSAT 5 TM work scenes selected for each area are outlined along with local area geology. The nature and composition of surface coatings on the rock types within the subproject areas are determined, along with the origin of the coatings and their genetic link to host rocks, geologic interpretations are related to remote sensing units discriminated on TM imagery. Image processing was done using an ESL VAX/IDIMS image processing system, field sampling, and observation. Aerial photographs were acquired to facilitate location on the ground and to aid stratigraphic differentiation.

Taranik, J. V.↗

NASA NTRS2004

Land Use and Change

The overall purpose of this training session is to familiarize Central American project cooperators with the remote sensing and image processing research that is being conducted by the NASA research team and to acquaint them with the data products being produced in the areas of Land Cover and Land Use Change and carbon modeling under the NASA SERVIR project. The training session, therefore, will be both informative and practical in nature. Specifically, the course will focus on the physics of remote sensing, various satellite and airborne sensors (Landsat, MODIS, IKONOS, Star-3i), processing techniques, and commercial off the shelf image processing software.

Irwin, Daniel E.↗

NASA NTRS1991

Optical correlator method and apparatus for particle image velocimetry processing

Young's fringes are produced from a double exposure image of particles in a flowing fluid by passing laser light through the film and projecting the light onto a screen. A video camera receives the image from the screen and controls a spatial light modulator. The spatial modulator has a two dimensional array of cells the transmissiveness of which are controlled in relation to the brightness of the corresponding pixel of the video camera image of the screen. A collimated beam of laser light is passed through the spatial light modulator to produce a diffraction pattern which is focused onto another video camera, with the output of the camera being digitized and provided to a microcomputer. The diffraction pattern formed when the laser light is passed through the spatial light modulator and is focused to a point corresponds to the two dimensional Fourier transform of the Young's fringe pattern projected onto the screen. The data obtained fro This invention was made with U.S. Government support awarded by the Department of the Army (DOD) and NASA grand number(s): DOD #DAAL03-86-K0174 and NASA #NAG3-718. The U.S. Government has certain rights in this invention.

Farrell, Patrick V.↗

NASA NTRS1984

The microcomputer workstation - An alternate hardware architecture for remotely sensed image analysis

Difficulties regarding the digital image analysis of remotely sensed imagery can arise in connection with the extensive calculations required. In the past, an expensive large to medium mainframe computer system was needed for performing these calculations. For image-processing applications smaller minicomputer-based systems are now used by many organizations. The costs for such systems are still in the range from $100K to $300K. Recently, as a result of new developments, the use of low-cost microcomputers for image processing and display systems appeared to have become feasible. These developments are related to the advent of the 16-bit microprocessor and the concept of the microcomputer workstation. Earlier 8-bit microcomputer-based image processing systems are briefly examined, and a computer workstation architecture is discussed. Attention is given to a microcomputer workstation developed by Stanford University, and the design and implementation of a workstation network.

Erickson, W. K.↗

NASA NTRS1989

Processing SAR Images On Board

Synthetic-aperture radar (SAR) processor operates aboard moving radar platform, turning raw signal data into images of scanned terrain. In conventional SAR systems, raw data either transmitted to stations on ground or recorded on magnetic tape by high-density digital recorders for subsequent processing on ground. Advantages include making SAR images available immediately and processed data transmitted or recorded only one-fifth as voluminous as raw data. Onboard image compression reduces volume of data even further.

Liu, Kuang Y.↗

NASA NTRS1992

Mapping biomass for a northern forest ecosystem using multi-frequency SAR data

Image processing methods for mapping standing biomass for a forest in Maine, using NASA/JPL airborne synthetic aperture radar (AIRSAR) polarimeter data, are presented. By examining the dependence of backscattering on standing biomass, it is determined that the ratio of HV backscattering from a longer wavelength (P- or L-band) to a shorter wavelength (C) is a good combination for mapping total biomass. This ratio enhances the correlation of the image signature to the standing biomass and compensates for a major part of the variations in backscattering attributed to radar incidence angle. The image processing methods used include image calibration, ratioing, filtering, and segmentation. The image segmentation algorithm uses both means and variances of the image, and it is combined with the image filtering process. Preliminary assessment of the resultant biomass maps suggests that this is a promising method.

Ranson, K. J.↗

NASA NTRS1984

Satellite land remote sensing advancements for the eighties; Proceedings of the Eighth Pecora Symposium, Sioux Falls, SD, October 4-7, 1983

Among the topics discussed are NASA's land remote sensing plans for the 1980s, the evolution of Landsat 4 and the performance of its sensors, the Landsat 4 thematic mapper image processing system radiometric and geometric characteristics, data quality, image data radiometric analysis and spectral/stratigraphic analysis, and thematic mapper agricultural, forest resource and geological applications. Also covered are geologic applications of side-looking airborne radar, digital image processing, the large format camera, the RADARSAT program, the SPOT 1 system's program status, distribution plans, and simulation program, Space Shuttle multispectral linear array studies of the optical and biological properties of terrestrial land cover, orbital surveys of solar-stimulated luminescence, the Space Shuttle imaging radar research facility, and Space Shuttle-based polar ice sounding altimetry.

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NASA NTRS2012

Smart Image Enhancement Process

Contrast and lightness measures are used to first classify the image as being one of non-turbid and turbid. If turbid, the original image is enhanced to generate a first enhanced image. If non-turbid, the original image is classified in terms of a merged contrast/lightness score based on the contrast and lightness measures. The non-turbid image is enhanced to generate a second enhanced image when a poor contrast/lightness score is associated therewith. When the second enhanced image has a poor contrast/lightness score associated therewith, this image is enhanced to generate a third enhanced image. A sharpness measure is computed for one image that is selected from (i) the non-turbid image, (ii) the first enhanced image, (iii) the second enhanced image when a good contrast/lightness score is associated therewith, and (iv) the third enhanced image. If the selected image is not-sharp, it is sharpened to generate a sharpened image. The final image is selected from the selected image and the sharpened image.

Jobson, Daniel J.↗

NASA NTRS1972

Digital Handling and Processing of Remote Sensing Data

Details of plans for developing a digital image processing facility to handle remote sensing data are reported. Also given are programs for data acquisition and handling, systems for use in programming various digital image processing tasks, and algorithms for feature enhancement.

Algazi, R.↗

NASA NTRS1978

Theory on data processing and instrumentation

A selection of NASA Earth observations programs are reviewed, emphasizing hardware capabilities. Sampling theory, noise and detection considerations, and image evaluation are discussed for remote sensor imagery. Vision and perception are considered, leading to numerical image processing. The use of multispectral scanners and of multispectral data processing systems, including digital image processing, is depicted. Multispectral sensing and analysis in application with land use and geographical data systems are also covered.

Billingsley, F. C.↗

NASA NTRS2011

Parallel Wavefront Analysis for a 4D Interferometer

This software provides a programming interface for automating data collection with a PhaseCam interferometer from 4D Technology, and distributing the image-processing algorithm across a cluster of general-purpose computers. Multiple instances of 4Sight (4D Technology s proprietary software) run on a networked cluster of computers. Each connects to a single server (the controller) and waits for instructions. The controller directs the interferometer to several images, then assigns each image to a different computer for processing. When the image processing is finished, the server directs one of the computers to collate and combine the processed images, saving the resulting measurement in a file on a disk. The available software captures approximately 100 images and analyzes them immediately. This software separates the capture and analysis processes, so that analysis can be done at a different time and faster by running the algorithm in parallel across several processors. The PhaseCam family of interferometers can measure an optical system in milliseconds, but it takes many seconds to process the data so that it is usable. In characterizing an adaptive optics system, like the next generation of astronomical observatories, thousands of measurements are required, and the processing time quickly becomes excessive. A programming interface distributes data processing for a PhaseCam interferometer across a Windows computing cluster. A scriptable controller program coordinates data acquisition from the interferometer, storage on networked hard disks, and parallel processing. Idle time of the interferometer is minimized. This architecture is implemented in Python and JavaScript, and may be altered to fit a customer s needs.

Rao, Shanti R.↗

NASA NTRS2002

The Statistics of Visual Representation

The experience of retinex image processing has prompted us to reconsider fundamental aspects of imaging and image processing. Foremost is the idea that a good visual representation requires a non-linear transformation of the recorded (approximately linear) image data. Further, this transformation appears to converge on a specific distribution. Here we investigate the connection between numerical and visual phenomena. Specifically the questions explored are: (1) Is there a well-defined consistent statistical character associated with good visual representations? (2) Does there exist an ideal visual image? And (3) what are its statistical properties?

Jobson, Daniel J.↗

NASA NTRS1996

(abstract) A High Throughput 3-D Inner Product Processor

A particularily challenging image processing application is the real time scene acquisition and object discrimination. It requires spatio-temporal recognition of point and resolved objects at high speeds with parallel processing algorithms. Neural network paradigms provide fine grain parallism and, when implemented in hardware, offer orders of magnitude speed up. However, neural networks implemented on a VLSI chip are planer architectures capable of efficient processing of linear vector signals rather than 2-D images. Therefore, for processing of images, a 3-D stack of neural-net ICs receiving planar inputs and consuming minimal power are required. Details of the circuits with chip architectures will be described with need to develop ultralow-power electronics. Further, use of the architecture in a system for high-speed processing will be illustrated.

imaging parallel processing algorithms linear vect↗

NASA NTRS1996

Processing Satellite Images on Tertiary Storage: A Study of the Impact of Tile Size on Performance

Before raw data from a satellite can be used by an Earth scientist, it must first undergo a number of processing steps including basic processing, cleansing, and geo-registration. Processing actually expands the volume of data collected by a factor of 2 or 3 and the original data is never deleted. Thus processing and storage requirements can exceed 2 terrabytes/day. Once processed data is ready for analysis, a series of algorithms (typically developed by the Earth scientists) is applied to a large number of images in a data set. The focus of this paper is how best to handle such images stored on tape using the following assumptions: (1) all images of interest to a scientist are stored on a single tape, (2) images are accessed and processed in the order that they are stored on tape, and (3) the analysis requires access to only a portion of each image and not the entire image.

Yu, JieBing↗

NASA NTRS1991

Image retrieval and processing system version 2.0 development work

The Image Retrieval and Processing System (IRPS) is a software package developed at Washington University and used by the NASA Regional Planetary Image Facilities (RPIF's). The IRPS combines data base management and image processing components to allow the user to examine catalogs of image data, locate the data of interest, and perform radiometric and geometric calibration of the data in preparation for analysis. Version 1.0 of IRPS was completed in Aug. 1989 and was installed at several IRPS's. Other RPIF's use remote logins via NASA Science Internet to access IRPS at Washington University. Work was begun on designing and population a catalog of Magellan image products that will be part of IRPS Version 2.0, planned for release by the end of calendar year 1991. With this catalog, a user will be able to search by orbit and by location for Magellan Basic Image Data Records (BIDR's), Mosaicked Image Data Records (MIDR's), and Altimetry-Radiometry Composite Data Records (ARCDR's). The catalog will include the Magellan CD-ROM volume, director, and file name for each data product. The image processing component of IRPS is based on the Planetary Image Cartography Software (PICS) developed by the U.S. Geological Survey, Flagstaff, Arizona. To augment PICS capabilities, a set of image processing programs were developed that are compatible with PICS-format images. This software includes general-purpose functions that PICS does not have, analysis and utility programs for specific data sets, and programs from other sources that were modified to work with PICS images. Some of the software will be integrated into the Version 2.0 release of IRPS. A table is presented that lists the programs with a brief functional description of each.

Slavney, Susan H.↗