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Review of some of the computer-aided digital image processing techniques recently developed. Special attention is given to mapping and mosaicking techniques and to preliminary developments in range determination from stereo image pairs. The discussed image processing utilization areas include space, biomedical, and robotic applications.
An imaging processing technique is developed for enhancement and calibration of imaging experiments. The technique is shown to be useful not only for the original application but also when applied to images from a wide variety of sources.
A mosaic of the Seasat Synthetic Aperture Radar (SAR) optically processed images over Central America is presented. A SAR image of the Rio Lacantum area (southeastern Mexico) has been digitally processed and its interpretation is presented. The region is characterized by low relief and a dense vegetation canopy. Surface is believed to be indicative of subsurface structural features. The Seasat-SAR system had a steep imaging geometry (incidence angle 23 + or - 3 deg off-nadir) which is favorable for detection of subtle topographic variations. Subtle textural features in the image corresponding to surface topography were enhanced by image processing techniques. A structural and lithologic interpretation of the processed images is presented. Lineaments oriented NE-SW dominate and intersect broad folds trending NW-SE. Distinctive karst topography characterizes one high relief area
Digital image processing techniques are typically used to produce improved digital images through the application of successive enhancement techniques to a given image or to generate quantitative data about the objects within that image. In support of and to assist researchers in a wide range of disciplines, e.g., interferometry, heavy rain effects on aerodynamics, and structure recognition research, it is often desirable to count objects in an image and compute their geometric properties. Therefore, an image analysis application package, focusing on a subset of image analysis techniques used for object recognition in binary images, was developed. This report describes the techniques and algorithms utilized in three main phases of the application and are categorized as: image segmentation, object recognition, and quantitative analysis. Appendices provide supplemental formulas for the algorithms employed as well as examples and results from the various image segmentation techniques and the object recognition algorithm implemented.
A schlieren imaging system that uses the sun as a light source was developed it) obtain direct flow-field images of shock waves of aircraft in flight. This system was used to study how shock waves evolve to form sonic booms. The image quality obtained was limited by several optical and mechanical factors. Converting the photographs to digital images and applying digital image-processing techniques greatly improved the final quality of the images and more clearly showed the shock structures.
We demonstrate the use of inbred imaging to locate crystals mounted in cryoloops and cryopreserved in a nitrogen gas stream at 100K. In the home laboratory crystals are clearly seen in the infrared images with light transmitting through the sample while irradiating the crystal from behind, and with illumination from a direction perpendicular to the direction of view. The crystals transmit and reflect infrared radiation differently from the surrounding mother liquor and loop. Because of differences in contrast between crystals and their surrounding mother liquor, it is possible to clearly identify the crystal position. In use at the synchrotron, with robotically mounted crystals the small depth of field of the lens required the recording of multiple images at different focal points. Image processing techniques were then used to produce a clear image of the crystal. The resulting infrared images and intensity profiles show that infrared imaging can be a powerful complement to visual imaging in locating crystals in cryocooled loops.
The possibility of using microscopic interferometric techniques to monitor the growth of protein crystals on the Space Station is studied. Digital image processing techniques are used to develop a system for the real-time analysis of microscopic interferograms of nucleation sites during protein crystal growth. Features of the optical setup and the image processing system are discussed and experimental results are presented.
Two cameras equipped with selenium sulfur slow scan vidicons were used in the orbital reconnaissance of Mars by the U.S. Spacecraft Mariner 9 and the performance characteristics of these devices are presented. Digital image processing techniques have been widely applied in the analysis of images of Mars and its satellites. Photometric and geometric distortion corrections, image detail enhancement and transformation to standard map projection have been routinely employed. More specializing applications included picture differencing, limb profiling, solar lighting corrections, noise removal, line plots and computer mosaics. Information on enhancements as well as important picture geometric information was stored in a master library. Display of the library data in graphic or numerical form was accomplished by a data management computer program.
San Diego State University and Environmental Systems Research Institute, with other agencies, have applied satellite imaging and image processing techniques to geographic information systems (GIS) updating. The resulting images display land use and are used by a regional planning agency for applications like mapping vegetation distribution and preserving wildlife habitats. The EOCAP program provides government co-funding to encourage private investment in, and to broaden the use of NASA-developed technology for analyzing information about Earth and ocean resources.
A computerized array processing system has been developed to reduce, analyze, display, and correlate a large number of orbital and earth-based geochemical, geophysical, and geological measurements of the moon on a global scale. The system supports the activities of a consortium of about 30 lunar scientists involved in data synthesis studies. The system was modeled after standard digital image-processing techniques but differs in that processing is performed with floating point precision rather than integer precision. Because of flexibility in floating-point image processing, a series of techniques that are impossible or cumbersome in conventional integer processing were developed to perform optimum interpolation and smoothing of data. Recently color maps of about 25 lunar geophysical and geochemical variables have been generated.
Computer image processing techniques have been applied to particle counting and sizing in electron microscope images. Distributions of particle sizes were computed for several images and compared to manually computed distributions. The results of these experiments indicate that automatic particle counting within a reasonable error and computer processing time is feasible. The significance of the results is that the tedious task of manually counting a large number of particles can be eliminated while still providing the scientist with accurate results.
Complex materials are being developed for use in future advanced aerospace systems. High temperature materials have been targeted as a major area of materials development. The development of composites consisting of ceramic matrix and ceramic fibers or whiskers is currently being aggressively pursued internationally. These new advanced materials are difficult and costly to produce; however, their low density and high operating temperature range are needed for the next generation of advanced aerospace systems. These materials represent a challenge to the nondestructive evaluation community. Video imaging techniques not only enhance the nondestructive evaluation, but they are also required for proper evaluation of these advanced materials. Specific research examples are given, highlighting the impact that video systems have had on the nondestructive evaluation of ceramics. An image processing technique for computerized determination of grain and pore size distribution functions from microstructural images is discussed. The uses of video and computer systems for displaying, evaluating, and interpreting ultrasonic image data are presented.
Field techniques for estimating vegetative biomass are labor intensive, and rarely are used to monitor changes in biomass over time. Remote-sensing offers an attractive alternative to field measurements; however, because there is no simple correspondence between encoded radiance in multispectral images and biomass, it is not possible to measure vegetative biomass directly from AVIRIS images. Ways to estimate vegetative biomass by identifying community types and then applying biomass scalars derived from field measurements are investigated. Field measurements of community-scale vegetative biomass can be made, at least for local areas, but it is not always possible to identify vegetation communities unambiguously using remote measurements and conventional image-processing techniques. Furthermore, even when communities are well characterized in a single image, it typically is difficult to assess the extent and nature of changes in a time series of images, owing to uncertainties introduced by variations in illumination geometry, atmospheric attenuation, and instrumental responses. Our objective is to develop an improved method based on spectral mixture analysis to characterize and identify vegetative communities, that can be applied to multi-temporal AVIRIS and other types of images. In previous studies, multi-temporal data sets (AVIRIS and TM) of Owens Valley, CA were analyzed and vegetation communities were defined in terms of fractions of reference (laboratory and field) endmember spectra. An advantage of converting an image to fractions of reference endmembers is that, although fractions in a given pixel may vary from image to image in a time series, the endmembers themselves typically are constant, thus providing a consistent frame of reference.
Two techniques for the classification of multispectral remotely sensed data - the image processing technique of texture features, which is modeled after the human visual system, and the ECHO (Extraction and Classification of Homogeneous Objects) numerical technique - are examined. These two spatial analysis techniques are compared using Landsat imagery of an area of Indiana as an example, and it is found that the numerical approach is superior in classification accuracy and more efficient computationally.
Two methodologies for detecting and mapping land cover changes in and around growing urban regions are being studied at Jet Propulsion Laboratory. Both approaches, primarily based on digital image processing techniques, have been developed to supplement data stored in an Image Based Information System (IBIS). The structure of this information system enables the depiction of land cover changes in image format concurrently with statistical reports in tabular form. To date the expansion of two urban areas, Houston, Texas and Orlando, Florida, has been monitored.
The development of thermographic inspection methods for use on aerospace structures is under investigation. Several different material systems, structural geometries and defect types have been included in this study so as to establish a baseline from which future Infrared Thermography(IRT) testing can be made. This study examines various thermal loading techniques in an attempt to enhance the likelihood of capturing and identifying critically sized flaws under 'non laboratory' actual working conditions. Qualification techniques and calibration standards are also being investigated to standardize the thermographic method. In conjunction with the thermographic inspections, advanced image processing techniques including digital filtering and neural networks have been investigated to increase the ability of detecting and sizing flaws. Here, the digitized thermographic images are mathematically manipulated through various filtering techniques and/or artificial neural mapping schemes to enhance its overall quality, permitting accurate flaw identification even when the signal-to-noise ratio is low.
The development of thermographic inspection methods for use on aerospace structures is under investigation. Several different material systems, structural geometries and defect types have been included in this study so as to establish a baseline from which future IRT testing can be made. This study examines various thermal loading techniques in an attempt to enhance the likelihood of capturing and identifying critically sized flaws under 'non-laboratory' actual working conditions. Qualification techniques and calibration standards are also being investigated to standardize the thermographic method. In conjunction with the thermographic inspections, advanced image processing techniques including digital filtering and neural networks have been investigated to increase the ability of 91 detecting and sizing flaws. Here, the digitized thermographic images are mathematically manipulated through various filtering techniques and/or artificial neural mapping schemes to enhance its overall quality, permitting accurate flaw identification even when the signal-to-noise ratio is low.
Software calibration data and an optimum sequence for image processing technique application are developed for use in processing of S-056 telescope images.