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All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Fractal Characterization of Hyperspectral Imagery

Two Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral images selected from the Los Angeles area, one representing urban and the other, rural, were used to examine their spatial complexity across their entire spectrum of the remote sensing data. Using the ICAMS (Image Characterization And Modeling System) software, we computed the fractal dimension values via the isarithm and triangular prism methods for all 224 bands in the two AVIRIS scenes. The resultant fractal dimensions reflect changes in image complexity across the spectral range of the hyperspectral images. Both the isarithm and triangular prism methods detect unusually high D values on the spectral bands that fall within the atmospheric absorption and scattering zones where signature to noise ratios are low. Fractal dimensions for the urban area resulted in higher values than for the rural landscape, and the differences between the resulting D values are more distinct in the visible bands. The triangular prism method is sensitive to a few random speckles in the images, leading to a lower dimensionality. On the contrary, the isarithm method will ignore the speckles and focus on the major variation dominating the surface, thus resulting in a higher dimension. It is seen where the fractal curves plotted for the entire bandwidth range of the hyperspectral images could be used to distinguish landscape types as well as for screening noisy bands.

Qiu, Hon-Iie↗
NASA NTRS2008

On Convergence of Development Costs and Cost Models for Complex Spaceflight Instrument Electronics

Development costs of a few recent spaceflight instrument electrical and electronics subsystems have diverged from respective heritage cost model predictions. The cost models used are Grass Roots, Price-H and Parametric Model. These cost models originated in the military and industry around 1970 and were successfully adopted and patched by NASA on a mission-by-mission basis for years. However, the complexity of new instruments recently changed rapidly by orders of magnitude. This is most obvious in the complexity of representative spaceflight instrument electronics' data system. It is now required to perform intermediate processing of digitized data apart from conventional processing of science phenomenon signals from multiple detectors. This involves on-board instrument formatting of computational operands from row data for example, images), multi-million operations per second on large volumes of data in reconfigurable hardware (in addition to processing on a general purpose imbedded or standalone instrument flight computer), as well as making decisions for on-board system adaptation and resource reconfiguration. The instrument data system is now tasked to perform more functions, such as forming packets and instrument-level data compression of more than one data stream, which are traditionally performed by the spacecraft command and data handling system. It is furthermore required that the electronics box for new complex instruments is developed for one-digit watt power consumption, small size and that it is light-weight, and delivers super-computing capabilities. The conflict between the actual development cost of newer complex instruments and its electronics components' heritage cost model predictions seems to be irreconcilable. This conflict and an approach to its resolution are addressed in this paper by determining the complexity parameters, complexity index, and their use in enhanced cost model.

Kizhner, Semion↗
NASA NTRS2007

Analysis of Performance of Stereoscopic-Vision Software

A team of JPL researchers has analyzed stereoscopic vision software and produced a document describing its performance. This software is of the type used in maneuvering exploratory robotic vehicles on Martian terrain. The software in question utilizes correlations between portions of the images recorded by two electronic cameras to compute stereoscopic disparities, which, in conjunction with camera models, are used in computing distances to terrain points to be included in constructing a three-dimensional model of the terrain. The analysis included effects of correlation- window size, a pyramidal image down-sampling scheme, vertical misalignment, focus, maximum disparity, stereo baseline, and range ripples. Contributions of sub-pixel interpolation, vertical misalignment, and foreshortening to stereo correlation error were examined theoretically and experimentally. It was found that camera-calibration inaccuracy contributes to both down-range and cross-range error but stereo correlation error affects only the down-range error. Experimental data for quantifying the stereo disparity error were obtained by use of reflective metrological targets taped to corners of bricks placed at known positions relative to the cameras. For the particular 1,024-by-768-pixel cameras of the system analyzed, the standard deviation of the down-range disparity error was found to be 0.32 pixel.

Kim, Won↗
NASA NTRS2001

Pre-Hardware Optimization of Spacecraft Image Processing Software Algorithms and Hardware Implementation

Spacecraft telemetry rates have steadily increased over the last decade presenting a problem for real-time processing by ground facilities. This paper proposes a solution to a related problem for the Geostationary Operational Environmental Spacecraft (GOES-8) image processing application. Although large super-computer facilities are the obvious heritage solution, they are very costly, making it imperative to seek a feasible alternative engineering solution at a fraction of the cost. The solution is based on a Personal Computer (PC) platform and synergy of optimized software algorithms and re-configurable computing hardware technologies, such as Field Programmable Gate Arrays (FPGA) and Digital Signal Processing (DSP). It has been shown in [1] and [2] that this configuration can provide superior inexpensive performance for a chosen application on the ground station or on-board a spacecraft. However, since this technology is still maturing, intensive pre-hardware steps are necessary to achieve the benefits of hardware implementation. This paper describes these steps for the GOES-8 application, a software project developed using Interactive Data Language (IDL) (Trademark of Research Systems, Inc.) on a Workstation/UNIX platform. The solution involves converting the application to a PC/Windows/RC platform, selected mainly by the availability of low cost, adaptable high-speed RC hardware. In order for the hybrid system to run, the IDL software was modified to account for platform differences. It was interesting to examine the gains and losses in performance on the new platform, as well as unexpected observations before implementing hardware. After substantial pre-hardware optimization steps, the necessity of hardware implementation for bottleneck code in the PC environment became evident and solvable beginning with the methodology described in [1], [2], and implementing a novel methodology for this specific application [6]. The PC-RC interface bandwidth problem for the class of applications with moderate input-output data rates but large intermediate multi-thread data streams has been addressed and mitigated. This opens a new class of satellite image processing applications for bottleneck problems solution using RC technologies. The issue of a science algorithm level of abstraction necessary for RC hardware implementation is also described. Selected Matlab functions already implemented in hardware were investigated for their direct applicability to the GOES-8 application with the intent to create a library of Matlab and IDL RC functions for ongoing work. A complete class of spacecraft image processing applications using embedded re-configurable computing technology to meet real-time requirements, including performance results and comparison with the existing system, is described in this paper.

Kizhner, Semion↗
NASA NTRS2005

Insect-Inspired Optical-Flow Navigation Sensors

Integrated circuits that exploit optical flow to sense motions of computer mice on or near surfaces ( optical mouse chips ) are used as navigation sensors in a class of small flying robots now undergoing development for potential use in such applications as exploration, search, and surveillance. The basic principles of these robots were described briefly in Insect-Inspired Flight Control for Small Flying Robots (NPO-30545), NASA Tech Briefs, Vol. 29, No. 1 (January 2005), page 61. To recapitulate from the cited prior article: The concept of optical flow can be defined, loosely, as the use of texture in images as a source of motion cues. The flight-control and navigation systems of these robots are inspired largely by the designs and functions of the vision systems and brains of insects, which have been demonstrated to utilize optical flow (as detected by their eyes and brains) resulting from their own motions in the environment. Optical flow has been shown to be very effective as a means of avoiding obstacles and controlling speeds and altitudes in robotic navigation. Prior systems used in experiments on navigating by means of optical flow have involved the use of panoramic optics, high-resolution image sensors, and programmable imagedata- processing computers.

Thakoor, Sarita↗
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 NTRS1992

High resolution images of P/Tempel 1 and P/Tempel 2 constructed from IRAS survey data

Infrared images of P/Tempel 1 and P/Tempel 2 were constructed from IRAS survey data using a computer algorithm based on the Maximum Correlation Method for Image Construction (Aumann et al, 1990). The resulting images are of sufficiently high quality and resolution to delineate coma and tail morphology, and permit accurate photometry of the total dust complex. Comparisons of the infrared colors and photometric profiles of Tempel 1 and Tempel 2 at similar heliocentric distances show that the grains produced by the two comets are quite similar in radiometric and dynamic properties. Tempel 1 is found to produce about 30 percent more dust in its coma and tail than Tempel 2. The comae of Tempel 1 and Tempel 2 are expanding with mean velocities of 5.8 plus or minus 0.07 and 6.1 plus or minus 0.17 m/sec respectively, indicative of the ejection of large grains. The IRAS cataloged infrared fluxes (Walker, 1986) are found to be underestimated by as much as a factor of three for the comets. Therefore, it is essential to create images of the comets to obtain meaningful IRAS photometry.

Walker, Russell G.↗
NASA NTRSDate not supplied

An Overview of Research in Nondestructive Evaluation at the National Aeronautics and Space Administration’s Langley Research Center

The primary focus of the nondestructive evaluation (NDE) effort at Langley Research Center (LaRC) is to pioneer advances in NDE and structural health monitoring (SHM) through fundamental scientific research, technology development, and transferring these to the scientific and aerospace communities. This research supports a variety of NASA missions in space operations, exploration and aeronautics. The work presented will focus on the development of advanced inspection systems to detect critical flaws in both metallic and non-metallic materials and structures for aerospace applications. The emphasis is on increasing the performance of inspection systems to provide defect detection and quantitative characterization of the material state. LaRC NDE utilizes expertise in computational ultrasonic, thermal and eddy current simulations to reduce measured responses to size and locations of flaws, establish technique limitations and optimize performance. This presentation will highlight research into five strategic areas: (1) The development of novel solutions using state-of-the-art NDE technologies such as eddy current, ultrasound, thermography and x-ray computed tomography; (2) Research in the development of unique NDE technologies such as terahertz, wavefield imaging and nonlinear ultrasound; (3) The application of both computational and analytic models to NDE problems; (4) The development of advanced analytic methods to interpret large complex data sets; and (5) Advanced methods of applying and interrogating sensors for structural health monitoring.

K Elliott Cramer↗
NASA NTRSDate not supplied

An Overview of Research in Nondestructive Evaluation at the National Aeronautics and Space Administration’s Langley Research Center

The primary focus of the nondestructive evaluation (NDE) effort at Langley Research Center (LaRC) is to pioneer advances in NDE and structural health monitoring (SHM) through fundamental scientific research, technology development, and transferring these to the scientific and aerospace communities. This research supports a variety of NASA missions in space operations, exploration and aeronautics. The work presented will focus on the development of advanced inspection systems to detect critical flaws in both metallic and non-metallic materials and structures for aerospace applications. The emphasis is on increasing the performance of inspection systems to provide defect detection and quantitative characterization of the material state. LaRC NDE utilizes expertise in computational ultrasonic, thermal and eddy current simulations to reduce measured responses to size and locations of flaws, establish technique limitations and optimize performance. This presentation will highlight research into five strategic areas: (1) The development of novel solutions using state-of-the-art NDE technologies such as eddy current, ultrasound, thermography and x-ray computed tomography; (2) Research in the development of unique NDE technologies such as terahertz, wavefield imaging and nonlinear ultrasound; (3) The application of both computational and analytic models to NDE problems; (4) The development of advanced analytic methods to interpret large complex data sets; and (5) Advanced methods of applying and interrogating sensors for structural health monitoring.

NDE↗
DOE OSTI2021

Multichannel Analysis of Surface Waves Accelerated (MASWAccelerated): Software for efficient surface wave inversion using MPI and GPUs

Multichannel Analysis of Surface Waves (MASW) is a technique frequently used in geotechnical engineering and engineering geophysics to infer 1D layered models of seismic shear wave velocities in the top tens to hundreds of meters of the subsurface. We aim to accelerate MASW calculations by capitalizing on modern computer hardware available in the workstations of most engineers: multiple cores and graphics processing units (GPUs). We propose new parallel and GPU accelerated algorithms for computing 1D MASW inversion, and provide software implementations in C using Message Passing Interface (MPI) and CUDA. These algorithms take advantage of sparsity that arises in the problem, and the work balance between processes considers typical data trends. We compare our methods to an existing open source Matlab MASW tool. Our serial C implementation achieves a 2x speedup over the Matlab software, and we continue to see improvements by parallelizing the problem with MPI. Here we see nearly perfect strong and weak scaling for uniform data, and improve strong scaling for realistic data by repartitioning the problem to process mapping. By utilizing GPUs available on most modern workstations, we observe an additional 1.3x speedup over the serial C implementation on the first use of the method. We typically repeatedly evaluate theoretical dispersion curves as part of an optimization procedure, and on the GPU the kernel can be cached for faster reuse on later runs. We observe a 3.2x speedup on the cached GPU runs compared to the serial C runs. This work is the first open-source parallel or GPU-accelerated software tool for MASW imaging, and should enable geotechnical engineers to fully utilize all computer hardware at their disposal.

58 GEOSCIENCES↗
NASA NTRS1997

Grapevine Remote Sensing Analysis of Phylloxera Early Stress (GRAPES): Remote Sensing Analysis Summary

High spatial resolution airborne imagery was acquired in California's Napa Valley in 1993 and 1994 as part of the Grapevine Remote sensing Analysis of Phylloxera Early Stress (GRAPES) project. Investigators from NASA, the University of California, the California State University, and Robert Mondavi Winery examined the application of airborne digital imaging technology to vineyard management, with emphasis on detecting the phylloxera infestation in California vineyards. Because the root louse causes vine stress that leads to grapevine death in three to five years, the infested areas must be replanted with resistant rootstock. Early detection of infestation and changing cultural practices can compensate for vine damage. Vineyard managers need improved information to decide where and when to replant fields or sections of fields to minimize crop financial losses. Annual relative changes in leaf area due to phylloxera infestation were determined by using information obtained from computing Normalized Difference Vegetation Index (NDVI) images. Two other methods of monitoring vineyards through imagery were also investigated: optical sensing of the Red Edge Inflection Point (REIP), and thermal sensing. These did not convey the stress patterns as well as the NDVI imagery and require specialized sensor configurations. NDVI-derived products are recommended for monitoring phylloxera infestations.

Lobitz, Brad↗
NASA NTRS1989

Physically-based parameterization of spatially variable soil and vegetation using satellite multispectral data

A stochastic-geometric landsurface reflectance model is formulated and tested for the parameterization of spatially variable vegetation and soil at subpixel scales using satellite multispectral images without ground truth. Landscapes are conceptualized as 3-D Lambertian reflecting surfaces consisting of plant canopies, represented by solid geometric figures, superposed on a flat soil background. A computer simulation program is developed to investigate image characteristics at various spatial aggregations representative of satellite observational scales, or pixels. The evolution of the shape and structure of the red-infrared space, or scattergram, of typical semivegetated scenes is investigated by sequentially introducing model variables into the simulation. The analytical moments of the total pixel reflectance, including the mean, variance, spatial covariance, and cross-spectral covariance, are derived in terms of the moments of the individual fractional cover and reflectance components. The moments are applied to the solution of the inverse problem: The estimation of subpixel landscape properties on a pixel-by-pixel basis, given only one multispectral image and limited assumptions on the structure of the landscape. The landsurface reflectance model and inversion technique are tested using actual aerial radiometric data collected over regularly spaced pecan trees, and using both aerial and LANDSAT Thematic Mapper data obtained over discontinuous, randomly spaced conifer canopies in a natural forested watershed. Different amounts of solar backscattered diffuse radiation are assumed and the sensitivity of the estimated landsurface parameters to those amounts is examined.

Jasinski, Michael F.↗
NASA NTRS2003

Hierarchical Image Segmentation of Remotely Sensed Data using Massively Parallel GNU-LINUX Software

A hierarchical set of image segmentations is a set of several image segmentations of the same image at different levels of detail in which the segmentations at coarser levels of detail can be produced from simple merges of regions at finer levels of detail. In [1], Tilton, et a1 describes an approach for producing hierarchical segmentations (called HSEG) and gave a progress report on exploiting these hierarchical segmentations for image information mining. The HSEG algorithm is a hybrid of region growing and constrained spectral clustering that produces a hierarchical set of image segmentations based on detected convergence points. In the main, HSEG employs the hierarchical stepwise optimization (HSWO) approach to region growing, which was described as early as 1989 by Beaulieu and Goldberg. The HSWO approach seeks to produce segmentations that are more optimized than those produced by more classic approaches to region growing (e.g. Horowitz and T. Pavlidis, [3]). In addition, HSEG optionally interjects between HSWO region growing iterations, merges between spatially non-adjacent regions (i.e., spectrally based merging or clustering) constrained by a threshold derived from the previous HSWO region growing iteration. While the addition of constrained spectral clustering improves the utility of the segmentation results, especially for larger images, it also significantly increases HSEG s computational requirements. To counteract this, a computationally efficient recursive, divide-and-conquer, implementation of HSEG (RHSEG) was devised, which includes special code to avoid processing artifacts caused by RHSEG s recursive subdivision of the image data. The recursive nature of RHSEG makes for a straightforward parallel implementation. This paper describes the HSEG algorithm, its recursive formulation (referred to as RHSEG), and the implementation of RHSEG using massively parallel GNU-LINUX software. Results with Landsat TM data are included comparing RHSEG with classic region growing.

Tilton, James C.↗
NASA NTRS1997

STRIPE: Remote Driving Using Limited Image Data

Driving a vehicle, either directly or remotely, is an inherently visual task. When heavy fog limits visibility, we reduce our car's speed to a slow crawl, even along very familiar roads. In teleoperation systems, an operator's view is limited to images provided by one or more cameras mounted on the remote vehicle. Traditional methods of vehicle teleoperation require that a real time stream of images is transmitted from the vehicle camera to the operator control station, and the operator steers the vehicle accordingly. For this type of teleoperation, the transmission link between the vehicle and operator workstation must be very high bandwidth (because of the high volume of images required) and very low latency (because delayed images can cause operators to steer incorrectly). In many situations, such a high-bandwidth, low-latency communication link is unavailable or even technically impossible to provide. Supervised TeleRobotics using Incremental Polyhedral Earth geometry, or STRIPE, is a teleoperation system for a robot vehicle that allows a human operator to accurately control the remote vehicle across very low bandwidth communication links, and communication links with large delays. In STRIPE, a single image from a camera mounted on the vehicle is transmitted to the operator workstation. The operator uses a mouse to pick a series of 'waypoints' in the image that define a path that the vehicle should follow. These 2D waypoints are then transmitted back to the vehicle, where they are used to compute the appropriate steering commands while the next image is being transmitted. STRIPE requires no advance knowledge of the terrain to be traversed, and can be used by novice operators with only minimal training. STRIPE is a unique combination of computer and human control. The computer must determine the 3D world path designated by the 2D waypoints and then accurately control the vehicle over rugged terrain. The human issues involve accurate path selection, and the prevention of disorientation, a common problem across all types of teleoperation systems. STRIPE is the only semi-autonomous teleoperation system that can accurately follow paths designated in monocular images on varying terrain. The thesis describes the STRIPE algorithm for tracking points using the incremental geometry model, insight into the design and redesign of the interface, an analysis of the effects of potential errors, details of the user studies, and hints on how to improve both the algorithm and interface for future designs.

REMOTE CONTROL↗
NASA NTRS1987

Ray tracing on the MPP

Generating graphics to faithfully represent information can be a computationally intensive task. A way of using the Massively Parallel Processor to generate images by ray tracing is presented. This technique uses sort computation, a method of performing generalized routing interspersed with computation on a single-instruction-multiple-data (SIMD) computer.

Dorband, John E.↗
NASA NTRS2010

Landmark Detection in Orbital Images Using Salience Histograms

NASA's planetary missions have collected, and continue to collect, massive volumes of orbital imagery. The volume is such that it is difficult to manually review all of the data and determine its significance. As a result, images are indexed and searchable by location and date but generally not by their content. A new automated method analyzes images and identifies "landmarks," or visually salient features such as gullies, craters, dust devil tracks, and the like. This technique uses a statistical measure of salience derived from information theory, so it is not associated with any specific landmark type. It identifies regions that are unusual or that stand out from their surroundings, so the resulting landmarks are context-sensitive areas that can be used to recognize the same area when it is encountered again. A machine learning classifier is used to identify the type of each discovered landmark. Using a specified window size, an intensity histogram is computed for each such window within the larger image (sliding the window across the image). Next, a salience map is computed that specifies, for each pixel, the salience of the window centered at that pixel. The salience map is thresholded to identify landmark contours (polygons) using the upper quartile of salience values. Descriptive attributes are extracted for each landmark polygon: size, perimeter, mean intensity, standard deviation of intensity, and shape features derived from an ellipse fit.

Wagstaff, Kiri L.↗
DOE OSTI2025

Quantum optical classifier with superexponential speedup

Abstract Classification is a central task in deep learning algorithms. Usually, images are first captured and then processed by a sequence of operations, of which the artificial neuron represents one of the fundamental units. This paradigm requires significant resources that scale (at least) linearly in the image resolution, both in terms of photons and computational operations. Here, we present a quantum optical pattern recognition method for binary classification tasks. It classifies objects without reconstructing their images, using the rate of two-photon coincidences at the output of a Hong-Ou-Mandel interferometer, where both the input and the classifier parameters are encoded into single-photon states. Our method exhibits the behaviour of a classical neuron of unit depth. Once trained, it shows a constant $${{\mathcal{O}}}(1)$$ O ( 1 ) complexity in the number of computational operations and photons required by a single classification. This is a superexponential advantage over a classical artificial neuron.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗