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NASA NTRS1993

Feature Extraction Based on Decision Boundaries

In this paper, a novel approach to feature extraction for classification is proposed based directly on the decision boundaries. We note that feature extraction is equivalent to retaining informative features or eliminating redundant features; thus, the terms 'discriminantly information feature' and 'discriminantly redundant feature' are first defined relative to feature extraction for classification. Next, it is shown how discriminantly redundant features and discriminantly informative features are related to decision boundaries. A novel characteristic of the proposed method arises by noting that usually only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is therefore introduced. Next, a procedure to extract discriminantly informative features based on a decision boundary is proposed. The proposed feature extraction algorithm has several desirable properties: (1) It predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and (2) it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal class means or equal class covariances as some previous algorithms do. Experiments show that the performance of the proposed algorithm compares favorably with those of previous algorithms.

Lee, Chulhee↗

NASA NTRS1984

Simple descriptor for contextual classification of hyperdimensional remotely sensed spectral data

An extended CIE transformation was employed to deal with data reduction problems of hyperdimensional spectral data for NASA airborne and Shuttle imaging spectrometers. A simple descriptor was found to be very effective in analyzing spectral data covers from 0.4 to 2.4 microns. Results reveal contextual properties of minerals, vegetation, and crops. It provides a means to monitor seasonal growth variations for crops. It can also serve as pseudocolor indexing for imaging spectrometer imagery extended beyond the visible range.

Chiou, W. C.↗

NASA NTRSDate not supplied

ImageLabler: Labeling and Managing Image Data for Machine Learning in the Earth Sciences

While machine learning techniques for image classification have been around for a long time, storing and managing the vast number of images required as training data is still a problem for scientists. This is especially true for the field of Earth science, where only recently have experts begun using machine learning techniques for image-based phenomena classification. Image Labeler, a fast and scalable cloud-based tagging platform for Earth science images, seeks to improve upon existing methods of managing images and associated metadata, such as maintaining categorized folders of images on a local machine, a process that can be cumbersome and difficult to scale. The platform facilitates rapid development of image-based Earth science phenomena training datasets by allowing scientists to upload their existing imagery as well as extract new samples from open satellite imagery services made available through NASA’s Global Imagery Browse Service (GIBS). Image Labeler also supports GeoTIFF data, with capabilities such as displaying GeoTIFFs on an interactive map, drawing shapefiles over them, and tagging them with additional metadata. This allows scientists to perform spatiotemporal subsetting with geographic information and develop training data more quickly. Built using modern web technologies, Image Labeler includes additional capabilities such as team collaboration for large-scale image tagging projects. Users can download their data in a machine-learning-ready format, allowing scientists to spend time on experimentation rather than on the collection of training data. In this presentation, we demonstrate how Image Labeler seeks to become a one-stop image data management solution for machine learning applications in Earth science.

Ashish Acharya↗

NASA NTRS1981

Delineation of soil temperature regimes from HCMM data

Evaluation of LANDSAT and Heat Capacity Mapping Mission (HCMM) data as input into National Cooperative Soil Survey is discussed. Signature classification techniques were applied to 13 May 76 LANDSAT data. LANDSAT data was overlaid with HCMM data, revealing registration problems caused by a shortage of control points in LANDSAT data, and the WARP program developed to improve registration accuracy. Initial images for control point selection were produced using digital terrain elevation data. Statistical procedures for evaluating data classification and to describe spatial distribution of surface temperature and its correlation with soil surface conditions were investigated.

Day, R. L.↗

NASA NTRS1994

Design of Ternary Correlation Filters to Reduce Probability of Error

The problem of designing ternary phase and amplitude filters (TPAF's) that reduce the probability of image misclassification for a two-class image set is studied. The Fisher ratio is used as a measure of the correct classification rate, and an attempt is made to maximize this quantity in the filter designs. Given the nonanalytical nature of the design problem, a simulated annealing optimization technique is employed. Computer simulation results are presented for several cases including single in-class and out-of-class image sets and multiple image sets corresponding to the design of synthetic discriminant function filters. Significant improvements are found in expected rates of correct classification in comparison to binary phase-only filters and other TPAF designs. Approaches to accelerate the filter design process are also discussed.

Downie, John D.↗

NASA NTRS1975

Maximum likelihood signature estimation

Maximum-likelihood estimates are discussed which are based on an unlabeled sample of observations, of unknown parameters in a mixture of normal distributions. Several successive approximation procedures for obtaining such maximum-likelihood estimates are described. These procedures, which are theoretically justified by the local contractibility of certain maps, are designed to take advantage of good initial estimates of the unknown parameters. They can be applied to the signature extension problem, in which good initial estimates of the unknown parameters are obtained from segments which are geographically near the segments from which the unlabeled samples are taken. Additional problems to which these methods are applicable include: estimation of proportions and adaptive classification (estimation of mean signatures and covariances).

Walker, H. F.↗

NASA NTRS1988

Space Station power system autonomy demonstration

The Systems Autonomy Demonstration Program (SADP) represents NASA's major effort to demonstrate, through a series of complex ground experiments, the application and benefits of applying advanced automation technologies to the Space Station project. Lewis Research Center (LeRC) and Marshall Space Flight Center (MSFC) will first jointly develop an autonomous power system using existing Space Station testbed facilities at each center. The subsequent 1990 power-thermal demonstration will then involve the cooperative operation of the LeRC/MSFC power system with the Johnson Space Center (JSC's) thermal control and DMS/OMS testbed facilities. The testbeds and expert systems at each of the NASA centers will be interconnected via communication links. The appropriate knowledge-based technology will be developed for each testbed and applied to problems requiring intersystem cooperation. Primary emphasis will be focused on failure detection and classification, system reconfiguration, planning and scheduling of electrical power resources, and integration of knowledge-based and conventional control system software into the design and operation of Space Station testbeds.

Kish, James A.↗

NASA NTRS1998

Smart Optical RAM for Fast Information Management and Analysis

Statement of Problem Instruments for high speed and high capacity in-situ data identification, classification and storage capabilities are needed by NASA for the information management and analysis of extremely large volume of data sets in future space exploration, space habitation and utilization, in addition to the various missions to planet-earth programs. Parameters such as communication delays, limited resources, and inaccessibility of human manipulation require more intelligent, compact, low power, and light weight information management and data storage techniques. New and innovative algorithms and architecture using photonics will enable us to meet these challenges. The technology has applications for other government and public agencies.

Liu, Hua-Kuang↗

NASA NTRS2014

A Classification Table for Achondrites

Classifying chondrites is relatively easy and the criteria are well documented. It is based on mineral compositions, textural characteristics and more recently, magnetic susceptibility. It can be more difficult to classify achondrites, especially those that are very similar to terrestrial igneous rocks, because mineralogical, textural and compositional properties can be quite variable. Achondrites contain essentially olivine, pyroxenes, plagioclases, oxides, sulphides and accessory minerals. Their origin is attributed to differentiated parents bodies: large asteroids (Vesta); planets (Mars); a satellite (the Moon); and numerous asteroids of unknown size. In most cases, achondrites are not eye witnessed falls and some do not have fusion crust. Because of the mineralogical and magnetic susceptibility similarity with terrestrial igneous rocks for some achondrites, it can be difficult for classifiers to confirm their extra-terrestrial origin. We -as classifiers of meteorites- are confronted with this problem with every suspected achondrite we receive for identification. We are developing a "grid" of classification to provide an easier approach for initial classification. We use simple but reproducible criteria based on mineralogical, petrological and geochemical studies. We presented the classes: acapulcoites, lodranites, winonaites and Martian meteorites (shergottite, chassignites, nakhlites). In this work we are completing the classification table by including the groups: angrites, aubrites, brachinites, ureilites, HED (howardites, eucrites, and diogenites), lunar meteorites, pallasites and mesosiderites. Iron meteorites are not presented in this abstract.

Chennaoui-Aoudjehane, H.↗

NASA NTRS1974

Classification, scale series and nomenclature of lunar charts

Lunar charts are classified with an eye toward solution of special problems of lunar cartography. The initial state of mapping the moon will require charts with scales of 1:5,000,000; 1:1,000,000; 1:250,000; 1:50,000; and 1:10,000.

Florenskiy, K. P.↗

NASA NTRS2016

Statistical Emulator for Expensive Classification Simulators

Expensive simulators prevent any kind of meaningful analysis to be performed on the phenomena they model. To get around this problem the concept of using a statistical emulator as a surrogate representation of the simulator was introduced in the 1980's. Presently, simulators have become more and more complex and as a result running a single example on these simulators is very expensive and can take days to weeks or even months. Many new techniques have been introduced, termed criteria, which sequentially select the next best (most informative to the emulator) point that should be run on the simulator. These criteria methods allow for the creation of an emulator with only a small number of simulator runs. We follow and extend this framework to expensive classification simulators.

Ross, Jerret↗

NASA NTRS1992

Magnetic Repeat Station Data

The nature, purpose, availability and distribution of magnetic repeat stations data are reviewed. Attention is paid to how repeat station data are obtained, sources of errors and limitations on accuracy. Repeat station results cannot achieve the accuracy of magnetic observatory estimates of the secular variation, but are, nevertheless, essential for improving the limited spatial coverage provided by observatories. The lack of appropriate standards and reporting procedures have meant that most global field molders have made little, or no, use of repeat data. Partly to rectify this problem, IAGA Working Group V-4 has instigated a scheme for systematic reporting and classification of magnetic repeat station data. The scheme will help to ensure that proper use is made of the wealth of repeat data that is available, and will also promote better observational practices.

Barton, C. E.↗

NASA NTRS1993

Classification of simple vegetation types using POLSAR image data

Mapping basic vegetation or land cover types is a fairly common problem in remote sensing. Knowledge of the land cover type is a key input to algorithms which estimate geophysical parameters, such as soil moisture, surface roughness, leaf area index or biomass from remotely sensed data. In an earlier paper, an algorithm for fitting a simple three-component scattering model to POLSAR data was presented. The algorithm yielded estimates for surface scatter, double-bounce scatter and volume scatter for each pixel in a POLSAR image data set. In this paper, we show how the relative levels of each of the three components can be used as inputs to simple classifier for vegetation type. Vegetation classes include no vegetation cover (e.g. bare soil or desert), low vegetation cover (e.g. grassland), moderate vegetation cover (e.g. fully developed crops), forest and urban areas. Implementation of the approach requires estimates for the three components from all three frequencies available using the NASA/JPL AIRSAR, i.e. C-, L- and P-bands. The research described in this paper was carried out by the Jet Propulsion Laboratory, California Institute of Technology under a contract with the National Aeronautics and Space Administration.

Freeman, A.↗

NASA NTRS1994

Neural network application to comprehensive engine diagnostics

We have previously reported on the use of neural networks for detection and identification of faults in complex microprocessor controlled powertrain systems. The data analyzed in those studies consisted of the full spectrum of signals passing between the engine and the real-time microprocessor controller. The specific task of the classification system was to classify system operation as nominal or abnormal and to identify the fault present. The primary concern in earlier work was the identification of faults, in sensors or actuators in the powertrain system as it was exercised over its full operating range. The use of data from a variety of sources, each contributing some potentially useful information to the classification task, is commonly referred to as sensor fusion and typifies the type of problems successfully addressed using neural networks. In this work we explore the application of neural networks to a different diagnostic problem, the diagnosis of faults in newly manufactured engines and the utility of neural networks for process control.

Marko, Kenneth A.↗

NASA NTRS1993

Classification of Simple Vegetation Types Using POLSAR Image Data

Freeman, A.↗

NASA NTRS1986

Incipient fault detection study for advanced spacecraft systems

A feasibility study to investigate the application of vibration monitoring to the rotating machinery of planned NASA advanced spacecraft components is described. Factors investigated include: (1) special problems associated with small, high RPM machines; (2) application across multiple component types; (3) microgravity; (4) multiple fault types; (5) eight different analysis techniques including signature analysis, high frequency demodulation, cepstrum, clustering, amplitude analysis, and pattern recognition are compared; and (6) small sample statistical analysis is used to compare performance by computation of probability of detection and false alarm for an ensemble of repeated baseline and faulted tests. Both detection and classification performance are quantified. Vibration monitoring is shown to be an effective means of detecting the most important problem types for small, high RPM fans and pumps typical of those planned for the advanced spacecraft. A preliminary monitoring system design and implementation plan is presented.

Milner, G. Martin↗

NASA NTRS1976

An ad hoc map evaluation procedure

An ad hoc map evaluation procedure is proposed which is most suitable for evaluating low-resolution classification maps against high resolution ground truth maps, such as maps against interpreted aircraft photographs. Commonly practiced sampling and evaluation procedures are impracticable in this context because of difficulties in registration and in comparing the samples. This ad hoc procedure is designed to overcome these two major problems, and its practicability is discussed. Two widely accepted parameters are estimated by the new procedure; namely, the probability of correct classification and the proportion biases. Statistical qualifications are also provided.

Kan, E. P.↗

NASA NTRS1987

Gaussian maximum likelihood and contextual classification algorithms for multicrop classification

The paper reviews some of the ways in which context has been handled in the remote-sensing literature, and additional possibilities are introduced. The problem of computing exhaustive and normalized class-membership probabilities from the likelihoods provided by the Gaussian maximum likelihood classifier (to be used as initial probability estimates to start relaxation) is discussed. An efficient implementation of probabilistic relaxation is proposed, suiting the needs of actual remote-sensing applications. A modified fuzzy-relaxation algorithm using generalized operations between fuzzy sets is presented. Combined use of the two relaxation algorithms is proposed to exploit context in multispectral classification of remotely sensed data. Results on both one artificially created image and one MSS data set are reported.

Di Zenzo, Silvano↗