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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.
A digital image compression preprocessor for use in a discrete cosine transform-based digital image compression device is provided. The preprocessor includes a gathering mechanism for determining discrete cosine transform statistics from input digital image data. A computing mechanism is operatively coupled to the gathering mechanism to calculate a image distortion array and a rate of image compression array based upon the discrete cosine transform statistics for each possible quantization value. A dynamic programming mechanism is operatively coupled to the computing mechanism to optimize the rate of image compression array against the image distortion array such that a rate-distortion-optimal quantization table is derived. In addition, a discrete cosine transform-based digital image compression device and a discrete cosine transform-based digital image compression and decompression system are provided. Also, a method for generating a rate-distortion-optimal quantization table, using discrete cosine transform-based digital image compression, and operating a discrete cosine transform-based digital image compression and decompression system are provided.
A comprehensive study has been undertaken to determine the extent to which ERTS-1 data could be used to detect, identify (classify), locate and measure features of applications interest in the disciplines of Agriculture and Forestry. The study areas included: six counties in five states in which were located examples of the most important crops and practices of American agriculture; and a portion of the Sam Houston National Forest, a typical Gulf coastal plain pine forest. The investigation utilized conventional image interpretation and computer-aided (spectral pattern recognition) analysis using both image products and computer compatible tapes. The emphasis was generally upon the computer-aided techniques. It was concluded that ERTS-1 data can be used to detect, identify, locate and measure a wide array of features of interest in agriculture and forestry.
Six channels of multispectral middle infrared (8 to 14 micron) aircraft scanner data were acquired over the East Tintic mining district, Utah. This area has high relief and moderate vegetation and consists mainly of Tertiary silicic igneous rocks and Paleozoic quartzite and carbonate rocks that have been locally hydrothermally altered. These digital-image data were computer processed to create a color-composite image based on principal component transformations. Color differences in this image are related to the spectral differences in the surface material and allow discrimination of several rock types, depending primarily on their silica content. When combined with a visible and near infrared color-composite image from a previous flight, with limited field checking, it is possible to discriminate quartzite, carbonate rocks, quartz latitic and quartz monzonitic rocks, latitic and monzonitic rocks, silicified altered rocks, argillized altered rocks, and vegetation.
Entire process under control of computer. Computer-controlled image-acquisition system tracks object, such as water bubble, as it moves in drop tube. Ultimately, such tracking system used to observe fusion-pellet formation in drop furnace.
Optical signal processing can be performed in either digital or analog systems. Digital computers and coherent optical systems are discussed as they are used in optical signal processing. Topics include: image restoration; phase-object visualization; image contrast reversal; optical computation; image multiplexing; and fabrication of spatial filters. Digital optical data processing deals with restoration of images degraded by signal-dependent noise. When the input data of an image restoration system are the numbers of photoelectrons received from various areas of a photosensitive surface, the data are Poisson distributed with mean values proportional to the illuminance of the incoherently radiating object and background light. Optical signal processing using coherent optical systems is also discussed. Following a brief review of the pertinent details of Ronchi's diffraction grating interferometer, moire effect, carrier-frequency photography, and achromatic holography, two new shearing interferometers based on them are presented. Both interferometers can produce variable shear.
Proposed system makes holographic interferograms quickly in wind tunnels. Holograms reveal two-dimensional flows around airfoils and provide information on distributions of pressure, structures of wake and boundary layers, and density contours of flow fields. Holograms form quickly in thermoplastic plates in wind tunnel. Plates rigid and left in place so neither vibrations nor photgraphic-development process degrades accuracy of holograms. System processes and analyzes images quickly. Semiautomatic micro-computer-based desktop image-processing unit now undergoing development moves easily to wind tunnel, and its speed and memory adequate for flows about airfoils.
For the extraction of topographic information about Venus from stereoradar images obtained from the Magellan Mission, a Synthetic Aperture Radar (SAR) compilation system was developed on analytical stereoplotters. The system software was extensively tested by using stereoradar images from various spacecraft and airborne radar systems, including Seasat, SIR-B, ERIM XCL, and STAR-1. Stereomodeling from radar images was proven feasible, and development is on a correct approach. During testing, the software was enhanced and modified to obtain more flexibility and better precision. Triangulation software for establishing control points by using SAR images was also developed through a joint effort with the Defense Mapping Agency. The SAR triangulation system comprises four main programs, TRIDATA, MODDATA, TRISAR, and SHEAR. The first two programs are used to sort and update the data; the third program, the main one, performs iterative statistical adjustment; and the fourth program analyzes the results. Also, input are flight data and data from the Global Positioning System and Inertial System (navigation information). The SAR triangulation system was tested with six strips of STAR-1 radar images on a VAX-750 computer. Each strip contains images of 10 minutes flight time (equivalent to a ground distance of 73.5 km); the images cover a ground width of 22.5 km. All images were collected from the same side. With an input of 44 primary control points, 441 ground control points were produced. The adjustment process converged after eight iterations. With a 6-m/pixel resolution of the radar images, the triangulation adjustment has an average standard elevation error of 81 m. Development of Magellan radargrammetry will be continued to convert both SAR compilation and triangulation systems into digital form.
Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.
Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.
Computer compatible tapes (CCTs) of LANDSAT 4 thematic mapper (TM) digital image products are compared and reviewed. The following tape formats are discussed: (1) raw band-sequential data (CCT-BT); (2) calibrated data (CCT-AT); and (3) geometrically resampled data (CCT-PT). Each format represents different steps in the process of producing fully corrected TM data. The CCT-BT images are uncorrected radiometrically or geometrically, CCT-AT data are radiometrically calibrated, and CCT-PT images are both radiometrically and geometrically corrected.
Geospatial resource assessments frequently require timely geospatial data processing that involves large multivariate remote sensing data sets. In particular, for disasters, response requires rapid access to large data volumes, substantial storage space and high performance processing capability. The processing and distribution of this data into usable information products requires a processing pipeline that can efficiently manage the required storage, computing utilities, and data handling requirements. In recent years, with the availability of cloud computing technology, cloud processing platforms have made available a powerful new computing infrastructure resource that can meet this need. To assess the utility of this resource, this project investigates cloud computing platforms for bulk, automated geoprocessing capabilities with respect to data handling and application development requirements. This presentation is of work being conducted by Applied Sciences Program Office at NASA-Stennis Space Center. A prototypical set of image manipulation and transformation processes that incorporate sample Unmanned Airborne System data were developed to create value-added products and tested for implementation on the "cloud". This project outlines the steps involved in creating and testing of open source software developed process code on a local prototype platform, and then transitioning this code with associated environment requirements into an analogous, but memory and processor enhanced cloud platform. A data processing cloud was used to store both standard digital camera panchromatic and multi-band image data, which were subsequently subjected to standard image processing functions such as NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index), band stacking, reprojection, and other similar type data processes. Cloud infrastructure service providers were evaluated by taking these locally tested processing functions, and then applying them to a given cloud-enabled infrastructure to assesses and compare environment setup options and enabled technologies. This project reviews findings that were observed when cloud platforms were evaluated for bulk geoprocessing capabilities based on data handling and application development requirements.
A combination of ab initio, atomistic and finite element methods (FEM) were used to investigate the structures, energetics and lattice thermal conductance of grain boundaries for the ultra high temperature ceramic ZrB2. Atomic models of idealized boundaries were relaxed using density functional theory. Information about bonding across the interfaces was determined from the electron localization function. The Kapitza conductance of larger scale versions of the boundary models were computed using non-equilibrium molecular dynamics. The interfacial thermal parameters together with single crystal thermal conductivities were used as parameters in microstructural computations. FEM meshes were constructed on top of microstructural images. From these computations, the effective thermal conductivity of the polycrystalline structure was determined.
Traditional computer-assisted image-analysis techniques in remote sensing lag well behind human abilities in terms of both speed and accuracy. A fundamental limitation of computer-assisted techniques is their inability to assimilate a variety of different data types leading to an interpretation in a manner similar to human image interpretation. Expert systems and computer-vision techniques are proposed as a potential solution to these limitations. Some aspects of human expertise in image analysis may be codified into expert systems. Image understanding and symbolic reasoning provide a means of assimilating spatial information and spatial reasoning into the analysis procedure. Knowledge-based image-analysis systems incorporate many of these concepts and have been implemented for some well defined problem domains. Geographic information systems represent an excellent environment for this type of analysis, providing both analytic tools and contextual information to the analysis procedure.
A gallery of what might be called the ""Best of HCMM'' imagery is presented. These 100 images, consisting mainly of Day-VIS, Day-IR, and Night-IR scenes plus a few thermal inertia images, were selected from the collection accrued in the Missions Utilization Office (Code 902) at the Goddard Space Flight Center. They were selected because of both their pictorial quality and their information or interest content. Nearly all the images are the computer processed and contrast stretched products routinely produced by the image processing facility at GSFC. Several LANDSAT images, special HCMM images made by HCMM investigators, and maps round out the input.
Helmet-mounted visual display system provides pilot with broad range of visual information for flight simulation. Offers nearly unlimited field of regard. Optical fibers transmit wide-angle images in response to motions of head. Two "pancake" lenses mounted on lightweight helmet. Cable of optical fibers carries images to each lens. "Light-valve" projectors deliver computer-generated binocular images to cables.
An image processing program is reported to analyze the high resolution images obtained by the balloon borne 36 in. diameter stratoscope 2 telescope. A film recording device is described which is capable of converting numerical data from the computer into photographic images.
The modern digital computer has made practical image processing techniques for handling nonlinear operations in both the geometrical and the intensity domains, various types of nonuniform noise cleanup, and the numerical analysis of pictures. An initial requirement is that a number of anomalies caused by the camera (e.g., geometric distortion, MTF roll-off, vignetting, and nonuniform intensity response) must be taken into account or removed to avoid their interference with the information extraction process. Examples illustrating these operations are discussed along with computer techniques used to emphasize details, perform analyses, classify materials by multivariate analysis, detect temporal differences, and aid in human interpretation of photos.