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NASA NTRS1980

Orbit/attitude estimation for the GOES spacecraft using VAS landmark data

A software system is described which provides for batch least squares estimation of spacecraft orbit, attitude, and camera bias parameters using image data from the Geostationary Operational Environmental Satellites (GOES). The image data are obtained by the Visible and Infrared Spin Scan Radiometer (VISSR) Atmospheric Sounder (VAS). The resulting estimated parameters are used for absolute image registration. Operating in the Digital Equipment Corporation (DEC) PDP-11/70 computer, the FORTRAN system also includes the capabilities of image display and manipulations. An overview of the system is presented as well as some numerical results obtained from observations taken by the SMS-2 satellite over a 3 day interval in August 1975.

Sielski, H.↗

NASA NTRS2005

The Optimization of Trained and Untrained Image Classification Algorithms for Use on Large Spatial Datasets

The HARVIST project seeks to automatically provide an accurate, interactive interface to predict crop yield over the entire United States. In order to accomplish this goal, large images must be quickly and automatically classified by crop type. Current trained and untrained classification algorithms, while accurate, are highly inefficient when operating on large datasets. This project sought to develop new variants of two standard trained and untrained classification algorithms that are optimized to take advantage of the spatial nature of image data. The first algorithm, harvist-cluster, utilizes divide-and-conquer techniques to precluster an image in the hopes of increasing overall clustering speed. The second algorithm, harvistSVM, utilizes support vector machines (SVMs), a type of trained classifier. It seeks to increase classification speed by applying a "meta-SVM" to a quick (but inaccurate) SVM to approximate a slower, yet more accurate, SVM. Speedups were achieved by tuning the algorithm to quickly identify when the quick SVM was incorrect, and then reclassifying low-confidence pixels as necessary. Comparing the classification speeds of both algorithms to known baselines showed a slight speedup for large values of k (the number of clusters) for harvist-cluster, and a significant speedup for harvistSVM. Future work aims to automate the parameter tuning process required for harvistSVM, and further improve classification accuracy and speed. Additionally, this research will move documents created in Canvas into ArcGIS. The launch of the Mars Reconnaissance Orbiter (MRO) will provide a wealth of image data such as global maps of Martian weather and high resolution global images of Mars. The ability to store this new data in a georeferenced format will support future Mars missions by providing data for landing site selection and the search for water on Mars.

Kocurek, Michael J.↗

NASA NTRS2007

Robotic Vision-Based Localization in an Urban Environment

A system of electronic hardware and software, now undergoing development, automatically estimates the location of a robotic land vehicle in an urban environment using a somewhat imprecise map, which has been generated in advance from aerial imagery. This system does not utilize the Global Positioning System and does not include any odometry, inertial measurement units, or any other sensors except a stereoscopic pair of black-and-white digital video cameras mounted on the vehicle. Of course, the system also includes a computer running software that processes the video image data. The software consists mostly of three components corresponding to the three major image-data-processing functions: Visual Odometry This component automatically tracks point features in the imagery and computes the relative motion of the cameras between sequential image frames. This component incorporates a modified version of a visual-odometry algorithm originally published in 1989. The algorithm selects point features, performs multiresolution area-correlation computations to match the features in stereoscopic images, tracks the features through the sequence of images, and uses the tracking results to estimate the six-degree-of-freedom motion of the camera between consecutive stereoscopic pairs of images (see figure). Urban Feature Detection and Ranging Using the same data as those processed by the visual-odometry component, this component strives to determine the three-dimensional (3D) coordinates of vertical and horizontal lines that are likely to be parts of, or close to, the exterior surfaces of buildings. The basic sequence of processes performed by this component is the following: 1. An edge-detection algorithm is applied, yielding a set of linked lists of edge pixels, a horizontal-gradient image, and a vertical-gradient image. 2. Straight-line segments of edges are extracted from the linked lists generated in step 1. Any straight-line segments longer than an arbitrary threshold (e.g., 30 pixels) are assumed to belong to buildings or other artificial objects. 3. A gradient-filter algorithm is used to test straight-line segments longer than the threshold to determine whether they represent edges of natural or artificial objects. In somewhat oversimplified terms, the test is based on the assumption that the gradient of image intensity varies little along a segment that represents the edge of an artificial object.

Mchenry, Michael↗

NASA NTRS1989

Image databases: Problems and perspectives

With the increasing number of computer graphics, image processing, and pattern recognition applications, economical storage, efficient representation and manipulation, and powerful and flexible query languages for retrieval of image data are of paramount importance. These and related issues pertinent to image data bases are examined.

Gudivada, V. Naidu↗

NASA NTRS1994

Automatic cataloguing and characterization of Earth science data using SE-trees

In the future, NASA's Earth Observing System (EOS) platforms will produce enormous amounts of remote sensing image data that will be stored in the EOS Data Information System. For the past several years, the Intelligent Data Management group at Goddard's Information Science and Technology Office has been researching techniques for automatically cataloguing and characterizing image data (ADCC) from EOS into a distributed database. At the core of the approach, scientists will be able to retrieve data based upon the contents of the imagery. The ability to automatically classify imagery is key to the success of contents-based search. We report results from experiments applying a novel machine learning framework, based on Set-Enumeration (SE) trees, to the ADCC domain. We experiment with two images: one taken from the Blackhills region in South Dakota; and the other from the Washington DC area. In a classical machine learning experimentation approach, an image's pixels are randomly partitioned into training (i.e. including ground truth or survey data) and testing sets. The prediction model is built using the pixels in the training set, and its performance is estimated using the testing set. With the first Blackhills image, we perform various experiments achieving an accuracy level of 83.2 percent, compared to 72.7 percent using a Back Propagation Neural Network (BPNN) and 65.3 percent using a Gaussain Maximum Likelihood Classifier (GMLC). However, with the Washington DC image, we were only able to achieve 71.4 percent, compared with 67.7 percent reported for the BPNN model and 62.3 percent for the GMLC.

Rymon, Ron↗

NASA NTRS2002

Summary of Quantitative Interpretation of Image Far Ultraviolet Auroral Data

Direct imaging of the magnetosphere by instruments on the IMAGE spacecraft is supplemented by simultaneous observations of the global aurora in three far ultraviolet (FUV) wavelength bands. The purpose of the multi-wavelength imaging is to study the global auroral particle and energy input from thc magnetosphere into the atmosphere. This paper describes provides the method for quantitative interpretation of FUV measurements. The Wide-Band Imaging Camera (WIC) provides broad band ultraviolet images of the aurora with maximum spatial and temporal resolution by imaging the nitrogen lines and bands between 140 and 180 nm wavelength. The Spectrographic Imager (SI), a dual wavelength monochromatic instrument, images both Doppler-shifted Lyman alpha emissions produced by precipitating protons, in the SI-12 channel and OI 135.6 nm emissions in the SI-13 channel. From the SI-12 Doppler shifted Lyman alpha images it is possible to obtain the precipitating proton flux provided assumptions are made regarding the mean energy of the protons. Knowledge of the proton (flux and energy) component allows the calculation of the contribution produced by protons in the WIC and SI-13 instruments. Comparison of the corrected WIC and SI-13 signals provides a measure of the electron mean energy, which can then be used to determine the electron energy fluxun-. To accomplish this reliable modeling emission modeling and instrument calibrations are required. In-flight calibration using early-type stars was used to validate the pre-flight laboratory calibrations and determine long-term trends in sensitivity. In general, very reasonable agreement is found between in-situ measurements and remote quantitative determinations.

Frey, H. U.↗

NASA NTRS1988

IUE's legacy for the future: The final archive and goals for its implementation

Requirements for the IUE archive, and how the signal/noise (S/N) ratio in photometrically corrected images can be enhanced considerably by cross-correlating the fixed pattern in a data image with that in a suitable flat-field image are described. From these cross-correlations it is feasible to derive an accurate geometrical correction to apply to the data image before applying the intensity transfer functions. The standard IUE processing software does not generate a sufficiently accurate geometric correction so that typical spatial errors of 1 to 2 pixels conspire with the large fixed pattern in raw images to produce significant misregistration noise. Tests on flat-field images demonstrate that an explicit geometric correction procedure can avoid most of the misregistration noise and can thereby improve the S/N ratio of IUE data by factors of 1.5 to 2.4.

Linsky, J. L.↗

NASA NTRS1994

A New Generation of Intelligent Trainable Tools for Analyzing Large Scientific Image Databases

In a variety of scientific disciplines two-dimensional digital image data is now relied on as a basic component of routine scientific investigation. The proliferation of image acquisition hardware such as multi-spectral remote-sensing platforms, medical imaging sensors, and high-resolution cameras have led to the widespread use of image data in fields such as atmospheric studies, planetary geology, ecology, agriculture, glacielogy, forestry, astronomy, diagnostic medicine, to name but a few.

machine↗

NASA NTRS2004

Hybrid Image-Plane/Stereo Manipulation

Hybrid Image-Plane/Stereo (HIPS) manipulation is a method of processing image data, and of controlling a robotic manipulator arm in response to the data, that enables the manipulator arm to place an end-effector (an instrument or tool) precisely with respect to a target (see figure). Unlike other stereoscopic machine-vision-based methods of controlling robots, this method is robust in the face of calibration errors and changes in calibration during operation. In this method, a stereoscopic pair of cameras on the robot first acquires images of the manipulator at a set of predefined poses. The image data are processed to obtain image-plane coordinates of known visible features of the end-effector. Next, there is computed an initial calibration in the form of a mapping between (1) the image-plane coordinates and (2) the nominal three-dimensional coordinates of the noted end-effector features in a reference frame fixed to the main robot body at the base of the manipulator. The nominal three-dimensional coordinates are obtained by use of the nominal forward kinematics of the manipulator arm that is, calculated by use of the currently measured manipulator joint angles and previously measured lengths of manipulator arm segments under the assumption that the arm segments are rigid, that the arm lengths are constant, and that there is no backlash. It is understood from the outset that these nominal three-dimensional coordinates are likely to contain possibly significant calibration errors, but the effects of the errors are progressively reduced, as described next. As the end-effector is moved toward the target, the calibration is updated repeatedly by use of data from newly acquired images of the end-effector and of the corresponding nominal coordinates in the manipulator reference frame. By use of the updated calibration, the coordinates of the target are computed in manipulator-reference-frame coordinates and then used to the necessary manipulator joint angles to position and orient the end-effector at the target with respect to the same kinematic model from the calibration step. As the end-effector/target distance decreases, the computed coordinates of the end-effector and target become more nearly affected by the same errors, so that the differences between their coordinates become increasingly precise. When the end-effector reaches the target, the remaining effective position error is the distance that corresponds to more than about one pixel in the stereoscopic images of the target.

Baumgartner, Eric↗

NASA NTRS2018

Digital Image Correlation Data Processing and Analysis Techniques to Enhance Test Data Assessment and Improve Structural Simulations

The NASA Shell Buckling Knockdown Factor Project (SBKF) was established in 2007 by the NASA Engineering and Safety Center (NESC) with the primary goal to develop new analysis-based buckling design factors (a.k.a. knockdown factors) and high-fidelity buckling simulations for selected launch-vehicle-like cylindrical shell structures. A series of tests are being conducted on large-scale metallic and composite cylindrical shells in order to provide validation data for these new factors and simulations. However, the validation of these new factors and simulations is quite demanding and requires test data that is commensurate with their fidelity. Traditional instrumentation, such as linear variable displacement transducers (LVDTs) and electrical-resistance strain gages serve a critical role in providing accurate displacement and strain measurements in these tests, but only allow for data to be recorded at a select number of point locations and are not sufficient to provide all the necessary validation data. Advanced measurement technologies can be used effectively to complement traditional instrumentation and gather additional data required to validate these structural simulations. In particular, three-dimensional digital image correlation (DIC) was implemented during SBKF cylinder testing to characterize the full-field displacement and strain behavior. Commercially available VIC-3DTM software and user-written data processing scripts were used to generate valuable data and insight into the complex buckling response of the cylinders that otherwise would be impossible to gather using traditional instrumentation. In addition, the measured data from DIC was used to verify measured test data obtained from other instrumentation, enhance test and analysis correlation, and help identify the root cause of anomalous test results that may have gone unexplained if only traditional instrumentation was used. Selected test results that demonstrate the use of DIC on the SBKF cylinders are presented and a portion of the data processing methods are described.

Gardner, Nathaniel W.↗

NASA NTRS1980

Basic cluster compression algorithm

Feature extraction and data compression of LANDSAT data is accomplished by BCCA program which reduces costs associated with transmitting, storing, distributing, and interpreting multispectral image data. Algorithm uses spatially local clustering to extract features from image data to describe spectral characteristics of data set. Approach requires only simple repetitive computations, and parallel processing can be used for very high data rates. Program is written in FORTRAN IV for batch execution and has been implemented on SEL 32/55.

Hilbert, E. E.↗

NASA NTRS1980

Digital preprocessing of SEASAT imagery

A model for radar image data is derived for use in developing enhancement techniques. This model describes the image data as the result of a multiplicative-convolved noise process. This information is then used to design a minimum mean square error (MMSE) filter. The resulting filter is implemented adaptively to change with local statistics. A radar image processing technique which provides the MMSE estimate inside homogeneous areas and tends to preserve edge structure is thus developed. This technique has been implemented and tested using digitally correlated SEASAT-A synthetic aperture radar (SAR) imagery.

Frost, V. S.↗

NASA NTRS1976

LANDSAT D position determination and correction study

An assessment of accuracy of the knowledge of LANDSAT D spacecraft ephemeris data, an evaluation of the impact of expected attitude and alignment accuracies and analysis of the various options for the combining of precision ephemeris and attitude data with scene image data are provided. The potential geometric correction system in order to determine overall system costs and impact on other system elements is characterized.

Source record↗

NASA NTRS1979

Multidimensional histograms

Program computes four dimensional histogram of Landsat multispectral image data. System enhances image handling and significantly reduces reprocessing costs.

Jayroe, R. R., Jr.↗

NASA NTRS1980

Satellite remote sensing facility for oceanograhic applications

The project organization, design process, and construction of a Remote Sensing Facility at Scripps Institution of Oceanography at LaJolla, California are described. The facility is capable of receiving, processing, and displaying oceanographic data received from satellites. Data are primarily imaging data representing the multispectral ocean emissions and reflectances, and are accumulated during 8 to 10 minute satellite passes over the California coast. The most important feature of the facility is the reception and processing of satellite data in real time, allowing investigators to direct ships to areas of interest for on-site verifications and experiments.

Evans, R. H.↗

NASA NTRS1983

Handbook on COMTAL's Image Processing System

An image processing system is the combination of an image processor with other control and display devices plus the necessary software needed to produce an interactive capability to analyze and enhance image data. Such an image processing system installed at NASA Langley Research Center, Instrument Research Division, Acoustics and Vibration Instrumentation Section (AVIS) is described. Although much of the information contained herein can be found in the other references, it is hoped that this single handbook will give the user better access, in concise form, to pertinent information and usage of the image processing system.

Faulcon, N. D.↗

NASA NTRS1996

WINDII atmospheric wave airglow imaging

Preliminary WINDII nighttime airglow wave-imaging data in the UARS rolldown attitude has been analyzed with the goal to survey gravity waves near the upper boundary of the middle atmosphere. Wave analysis is performed on O[sub 2](0,0) emissions from a selected 1[sup 0] x 1[sup 0] oblique view of the airglow layer at approximately 95 km altitude, which has no direct earth background and only an atmospheric background which is optically thick for the 0[sub 2](0,0) emission. From a small data set, orbital imaging of atmospheric wave structures is demonstrated, with indication of large variations in wave activity across land and sea. Comparison ground-based imagery is discussed with respect to similarity of wave variations across land/sea boundaries and future orbital mosaic image construction.

Troposphere↗

NASA NTRS2003

System and method for the adaptive mapping of matrix data to sets of polygons

A system and method for converting bitmapped data, for example, weather data or thermal imaging data, to polygons is disclosed. The conversion of the data into polygons creates smaller data files. The invention is adaptive in that it allows for a variable degree of fidelity of the polygons. Matrix data is obtained. A color value is obtained. The color value is a variable used in the creation of the polygons. A list of cells to check is determined based on the color value. The list of cells to check is examined in order to determine a boundary list. The boundary list is then examined to determine vertices. The determination of the vertices is based on a prescribed maximum distance. When drawn, the ordered list of vertices create polygons which depict the cell data. The data files which include the vertices for the polygons are much smaller than the corresponding cell data files. The fidelity of the polygon representation can be adjusted by repeating the logic with varying fidelity values to achieve a given maximum file size or a maximum number of vertices per polygon.

Burdon, David↗