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NASA NTRS1981

Comet nucleus - Some characteristics and a hypothesis on origin and structure

The spectroscopic properties of comets are discussed, and consideration is given to the problems of the cosmic ray irradiation and the origin and structure of cometary nuclei. The classification of cometary spectra as continuum dominant, molecular emissions dominant and CO(+) dominant is examined, and changes in spectral characteristics with heliocentric distance although not with cometary age are pointed out. Spectral evidence for a variety of organic compounds in the cometary nucleus is noted, and a scenario is presented whereby these complex molecules may form as a result of cosmic ray irradiation of cometary ices in the Oort cloud. Finally, a model is proposed for the accumulation of small icy grains to form a cometary nucleus composed of cometesimals of size distribution following a -5th power dependence on mass.

Donn, B.↗

NASA NTRS1989

Fundamentals of tribology at the atomic level

Tribology, the science and engineering of solid surfaces in moving contact, is a field that encompasses many disciplines: solid state physics, chemistry, materials science, and mechanical engineering. In spite of the practical importance and maturity of the field, the fundamental understanding of basic phenomena has only recently been attacked. An attempt to define some of these problems and indicate some profitable directions for future research is presented. There are three broad classifications: (1) fluid properties (compression, rheology, additives and particulates); (2) material properties of the solids (deformation, defect formation and energy loss mechanisms); and (3) interfacial properties (adhesion, friction chemical reactions, and boundary films). Research in the categories has traditionally been approached by considering macroscopic material properties. Recent activity has shown that some issues can be approached at the atomic level: the atoms in the materials can be manipulated both experimentally and theoretically, and can produce results related to macroscopic phenomena.

Ferrante, John↗

NASA NTRS1989

Designers as users - Design supports based on crew system design practices

The behavioral processes in aviation crew system design are theoretically discussed. Interviews with designers concerning actual design experiences were conducted in a structured manner, focusing on the nature of the design problem, the designers's tasks, how decisions governing the human operator's role are made, and how this decision making can be supported. Two taxonomies emerge, one of which summarizes the nature of the design problem and the second of which summarizes the tasks that designers perform toward solving the problem. Combining the taxonomies yields a design problem space that is used to classify existing forms of support. The results of this classification and the implications for needed supports are discussed.

Cody, William J.↗

NASA NTRS1965

Notes on the restricted three body problem- approximate behavior of solutions near the collinear lagrangian points

Studies the solutions of the restricted three-body problem near those equilibrium points which are collinear with the two positive masses. This is done to gain insight toward the development of an analytic proof and classification of the periodic orbits that pass near these equilibrium points, which have been discovered numerically by M. Davidson, and also to hopefully gain insight into the nature of solutions of the restricted three-body problem in general. The qualitative observations that are made are all deduced from the linearized equations.

Approximation method↗

NASA NTRS1977

Lake water quality mapping from LANDSAT

The lakes in three LANDSAT scenes were mapped by the Bendix MDAS multispectral analysis system. Field checking the maps by three separate individuals revealed approximately 90-95% correct classification for the lake categories selected. Variations between observers was about 5%. From the MDAS color coded maps the lake with the worst algae problem was easily located. This lake was closely checked and a pollution source of 100 cows was found in the springs which fed this lake. The theory, lab work and field work which made it possible for this demonstration project to be a practical lake classification procedure are presented.

Scherz, J. P.↗

NASA NTRS2019

Image Processing Methods for Coronal Hole Segmentation, Matching, and Map Classification

The paper presents the results from a multi-year effort to develop and validate image processing methods forselecting the best physical models based on solar image observations. The approach consists of selecting the physical models based on their agreement with coronal holes extracted from the images. Ultimately, the goal is to use physical models to predict geomagnetic storms. We decompose the problem into three subproblems: (i) coronal hole segmentation based on physical constraints, (ii) matching clusters of coronal holes between different maps, and (iii) physical map classification. For segmenting coronal holes, we develop a multi-modal method that uses segmentation maps from three different methods to initialize a level-set method that evolves the initial coronal hole segmentation to the magnetic boundary. Then, we introducea new method based on Linear Programming for matchingclusters of coronal holes. The final matching is then performedusing Random Forests. The methods were carefully validatedusing consensus maps derived from multiple readers, manualclustering, manual map classification, and method validation for50 maps. The proposed multi-modal segmentation method significantly outperformed SegNet, U-net, Henney-Harvey, and FCNby providing accurate boundary detection. Overall, the methodgave a 95.5% map classification accuracy.

Jatla, Venkatesh↗

NASA NTRS1975

Statistical theory and methodology for remote sensing data analysis with special emphasis on LACIE

Crop proportion estimators for determining crop acreage through the use of remote sensing were evaluated. Several studies of these estimators were conducted, including an empirical comparison of the different estimators (using actual data) and an empirical study of the sensitivity (robustness) of the class of mixture estimators. The effect of missing data upon crop classification procedures is discussed in detail including a simulation of the missing data effect. The final problem addressed is that of taking yield data (bushels per acre) gathered at several yield stations and extrapolating these values over some specified large region. Computer programs developed in support of some of these activities are described.

Odell, P. L.↗

NASA NTRS1975

Geomorphic interpretation of Skylab photography collected over the Nevada portion of the Great Basin

Skylab S190B black and white photography has a useful reconnaissance capability in geomorphic mapping of landform features in the arid and semi-arid Great Basin. Enlargement of original photographic data products to a scale of 1:250,000 scale compatible with published topographic maps permits ready identification and classification of most landform elements. However, interpretation suffers through the lack of stereoscopic coverage and introduces problems relating to scale and loss of detail. When aircraft underflight photography for the same area is used as a data enhancement tool, problems of scale, detail, and interpretation are diminished. The combination of orbital and underflight photographic coverage provides a regional overview in which the interrelationships of both micro- and macro-scale landforms become apparent.

Frater, J. B.↗

NASA NTRS1977

Status report: Data management program algorithm evaluation activity at Marshall Space Flight Center

An algorithm evaluation activity was initiated to study the problems associated with image processing by assessing the independent and interdependent effects of registration, compression, and classification techniques on LANDSAT data for several discipline applications. The objective of the activity was to make recommendations on selected applicable image processing algorithms in terms of accuracy, cost, and timeliness or to propose alternative ways of processing the data. As a means of accomplishing this objective, an Image Coding Panel was established. The conduct of the algorithm evaluation is described.

Jayroe, R. R., Jr.↗

NASA NTRS1973

Unsupervised classification techniques as components of a data and information system

The phenomenal increase in the amount of data and information being generated by remote sensing systems is stressed. A total system design approach as a solution to this problem is discussed with specific reference to the data and information system needs for Sortie Lab - a multiple use payload for the Shuttle. The development of a multispectral data processing system as a needed component of such a system is reviewed with emphasis on unsupervised multispectral classification techniques developed and presently in use at Marshall Space Flight Center.

Cummings, R. E.↗

NASA NTRS1988

Multiple directed graph large-class multi-spectral processor

Numerical analysis techniques for the interpretation of high-resolution imaging-spectrometer data are described and demonstrated. The method proposed involves the use of (1) a hierarchical classifier with a tree structure generated automatically by a Fisher linear-discriminant-function algorithm and (2) a novel multiple-directed-graph scheme which reduces the local maxima and the number of perturbations required. Results for a 500-class test problem involving simulated imaging-spectrometer data are presented in tables and graphs; 100-percent-correct classification is achieved with an improvement factor of 5.

Casasent, David↗

NASA NTRS2019

Feature Learning for Multispectral Satellite Imagery Classification Using Neural Architecture Search

Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such problems. However, building deep learning models to process the data requires expert knowledge of machine learning. We introduce DELTA, a software toolkit to bridge this technical gap and make deep learning easily accessible to earth scientists. Visual feature engineering is a critical part of the machine learning lifecycle, and hence is a key area that will be automated by DELTA. Hand-engineered features can perform well, but require a cross functional team with expertise in both machine learning and the specific problem domain, which is costly in both researcher time and labor. The problem is more acute with multispectral satellite imagery, which requires considerable computational resources to process. In order to automate the feature learning process, a neural architecture search samples the space of asymmetric and symmetric autoencoders using evolutionary algorithms. Since denoising autoencoders have been shown to perform well for feature learning, the autoencoders are trained on various levels of noise and the features generated by the best performing autoencoders evaluated according to their performance on image classification tasks. The resulting features are demonstrated to be effective for Landsat-8 flood mapping, as well as benchmark datasets CIFAR10 and SVHN.

Robert Campbell↗

NASA NTRS1990

Optical information processing for NASA's space exploration

The development status of optical processing techniques under development at NASA-JPL, NASA-Ames, and NASA-Johnson, is evaluated with a view to their potential applications in future NASA planetary exploration missions. It is projected that such optical processing systems can yield major reductions in mass, volume, and power requirements relative to exclusively electronic systems of comparable processing capabilities. Attention is given to high-order neural networks for distortion-invariant classification and pattern recognition, multispectral imaging using an acoustooptic tunable filter, and an optical matrix processor for control problems.

Chao, Tien-Hsin↗

NASA NTRS1992

Large-gap magnetic suspension systems

The classification of magnetic suspension devices into small-gap and large-gap categories is addressed. The relative problems of position sensing, control systems, power supplies, electromagnets, and magnetic field or force analysis are discussed. The similarity of all systems from a controls standpoint is qualified. Some applications where large-gap technology is being applied to systems with a physically small air-gap are mentioned. Finally, the applicability of some other suspension approaches, such as electrodynamic or superconducting are briefly addressed.

Britcher, Colin P.↗

NASA NTRS1993

Physics of solar activity

The aim of the research activity was to increase our understanding of solar activity through data analysis, theoretical analysis, and computer modeling. Because the research subjects were diverse and many researchers were supported by this grant, a select few key areas of research are described in detail. Areas of research include: (1) energy storage and force-free magnetic field; (2) energy release and particle acceleration; (3) radiation by nonthermal electrons; (4) coronal loops; (5) flare classification; (6) longitude distributions of flares; (7) periodicities detected in the solar activity; (8) coronal heating and related problems; and (9) plasma processes.

Sturrock, Peter A.↗

NASA NTRS1982

A means for utilizing ancillary information in multispectral classification

Attention is given to a method which makes it possible to incorporate information from ancillary data sources into the results of an existing classification of remotely sensed data. The method is based upon probabilistic label relaxation procedures which are used for imbedding spatial context data in image-labeling problems. The source of ancillary information is utilized in the form of a set of probabilities. These are introduced into a modified relaxation method called supervised relaxation labeling which, on application, develops a labeling for remotely sensed data that strikes a balance in consistency between spectral, spatial, and ancillary data sources of information. A description is given of results of a forestry classification in which accuracy is improved from 68% to 81% by incorporating topographic elevation in the outlined manner.

Richards, J. A.↗

NASA NTRS1982

Modeling misregistration and related effects on multispectral classification

Any noise in measurements (due to the scene, sensor, or the analog to digital process) causes a finite fraction of measurements to fall outside of the classification limits. For field boundaries, where the misregistration effects are felt, the misregistration causes the border in a given (set of) band(s) to be closer than expected to a given pixel, so that the mixed materials in the pixels cause additional pixels to fall outside of the class limits. Considerations of the transient distance involved in the difference in brightness between adjacent fields, when scaled to "per pixel", allow the estimation of the width of the border zones. The entire problem is then scaled to field sizes to allow estimation of the global effects. This approach allows the estimation of the accuracy of multispectral classification which might be expected for field interiors, the useful number of quantization bits, and one set of criteria for an unbiased classifier.

Billingsley, F. C.↗

NASA NTRS2000

Fusion of AIRSAR and TM Data for Parameter Classification and Estimation in Dense and Hilly Forests

The expanded remotely sensed data space consisting of coincident radar backscatter and optical reflectance data provides for a more complete description of the Earth surface. This is especially useful where many parameters are needed to describe a certain scene, such as in the presence of dense and complex-structured vegetation or where there is considerable underlying topography. The goal of this paper is to use a combination of radar and optical data to develop a methodology for parameter classification for dense and hilly forests, and further, class-specific parameter estimation. The area to be used in this study is the H. J. Andrews Forest in Oregon, one of the Long-Term Ecological Research (LTER) sites in the US. This area consists of various dense old-growth conifer stands, and contains significant topographic relief. The Andrews forest has been the subject of many ecological studies over several decades, resulting in an abundance of ground measurements. Recently, biomass and leaf-area index (LAI) values for approximately 30 reference stands have also become available which span a large range of those parameters. The remote sensing data types to be used are the C-, L-, and P-band polarimetric radar data from the JPL airborne SAR (AIRSAR), the C-band single-polarization data from the JPL topographic SAR (TOPSAR), and the Thematic Mapper (TM) data from Landsat, all acquired in late April 1998. The total number of useful independent data channels from the AIRSAR is 15 (three frequencies, each with three unique polarizations and amplitude and phase of the like-polarized correlation), from the TOPSAR is 2 (amplitude and phase of the interferometric correlation), and from the TM is 6 (the thermal band is not used). The range pixel spacing of the AIRSAR is 3.3m for C- and L-bands and 6.6m for P-band. The TOPSAR pixel spacing is 10m, and the TM pixel size is 30m. To achieve parameter classification, first a number of parameters are defined which are of interest to ecologists for forest process modeling. These parameters include total biomass, leaf biomass, LAI, and tree height. The remote sensing data from radar and TM are used to formulate a multivariate analysis problem given the ground measurements of the parameters. Each class of each parameter is defined by a probability density function (pdf), the spread of which defines the range of that class. High classification accuracy results from situations in which little overlap occurs between pdfs. Classification results provide the basis for the future work of class-specific parameter estimation using radar and optical data. This work was performed in part by the Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, and in part by the NASA Ames Research Center, Moffett Field, CA, both under contract from the National Aeronautics and Space Administration.

Moghaddam, Mahta↗