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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
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NASA NTRS1973

The use of the modified Cholesky decomposition in divergence and classification calculations

The use of the Cholesky decomposition technique is analyzed as applied to the feature selection and classification algorithms used in the analysis of remote sensing data (e.g. as in LARSYS). This technique is approximately 30% faster in classification and a factor of 2-3 faster in divergence, as compared with LARSYS. Also numerical stability and accuracy are slightly improved. Other methods necessary to deal with numerical stablity problems are briefly discussed.

Vanroony, D. L.↗
NASA NTRS1974

Some after-dinner reflections on system identification

The growth of the field of system identification is discussed along with changes in methodology which have taken place in recent years. The similarity between pattern recognition and system identification is pointed out, involving the modelling in the latter and the feature selection problem in the former. It is stated that once a model is formulated, including the disturbances and measurement errors, the parameter finding can be formulated as a statistical estimation problem. The various techniques and their application are discussed.

Balakrishnan, A. V.↗
NASA NTRS1974

ASTEP user's guide and software documentation

The Algorithm Simulation Test and Evaluation Program (ASTEP) is a modular computer program developed for the purpose of testing and evaluating methods of processing remotely sensed multispectral scanner earth resources data. ASTEP is written in FORTRAND V on the UNIVAC 1110 under the EXEC 8 operating system and may be operated in either a batch or interactive mode. The program currently contains over one hundred subroutines consisting of data classification and display algorithms, statistical analysis algorithms, utility support routines, and feature selection capability. The current program can accept data in LARSC1, LARSC2, ERTS, and Universal formats, and can output processed image or data tapes in Universal format.

Gliniewicz, A. S.↗
NASA NTRS1973

Machine processing of remotely sensed data; Proceedings of the Conference, Purdue University, West Lafayette, Ind., October 16-18, 1973

Topics discussed include the management and processing of earth resources information, special-purpose processors for the machine processing of remotely sensed data, digital image registration by a mathematical programming technique, the use of remote-sensor data in land classification (in particular, the use of ERTS-1 multispectral scanning data), the use of remote-sensor data in geometrical transformations and mapping, earth resource measurement with the aid of ERTS-1 multispectral scanning data, the use of remote-sensor data in the classification of turbidity levels in coastal zones and in the identification of ecological anomalies, the problem of feature selection and the classification of objects in multispectral images, the estimation of proportions of certain categories of objects, and a number of special systems and techniques. Individual items are announced in this issue.

Source record↗
NASA NTRS1973

The use of the modified Cholesky decomposition in divergence and classification calculations

This report analyzes the use of the modified Cholesky decomposition technique as applied to the feature selection and classification algorithms used in the analysis of remote sensing data (e.g., as in LARSYS). This technique is approximately 30% faster in classification and a factor of 2-3 faster in divergence, as compared with LARSYS. Also numerical stability and accuracy are slightly improved. Other methods necessary to deal with numerical stability problems are briefly discussed.

Van Rooy, D. L.↗
NASA NTRS1973

Feature extraction of multispectral data

A method is presented for feature extraction of multispectral scanner data. Non-training data is used to demonstrate the reduction in processing time that can be obtained by using feature extraction rather than feature selection.

Crane, R. B.↗
NASA NTRS1973

Adaptive on-line classification of multi-spectral scanner data

A possible solution to the analysis of the massive amounts of multi-spectral scanner data from the Earth Resource Technical Satellite (ERTS) program is proposed. This solution is offered as an adaptive on-line classification scheme. The classifier is described as well as its controller which is based on ground truth data. Cluster analysis is presented as an alternative approach to the ground truth data. Adaptive feature selection is discussed and possible mini-computer implementations are offered.

Fromm, F. R.↗
NASA NTRS1974

New theoretical models and ratio imaging techniques associated with the NASA earth resources spectral information system

Four independent investigations are reported; in general these are concerned with improving and utilizing the correlation between the physical properties of natural materials as evidenced in laboratory spectra and spectral data collected by multispectral scanners. In one investigation, two theoretical models were devised that permit the calculation of spectral emittance spectra for rock and mineral surfaces of various particle sizes. The simpler of the two models can be used to qualitatively predict the effect of texture on the spectral emittance of rocks and minerals; it is also potentially useful as an aid in predicting the identification of natural atmospheric aerosol constituents. The second investigation determined, via an infrared ratio imaging technique, the best pair of infrared filters for silicate rock-type discrimination. In a third investigation, laboratory spectra of natural materials were compressed into 11-digit ratio codes for use in feature selection, in searches for false alarm candidates, and eventually for use as training sets in completely automatic data processors. In the fourth investigation, general outlines of a ratio preprocessor and an automatic recognition map processor are developed for on-board data processing in the space shuttle era.

Vincent, R. K.↗
NASA NTRS1974

Pattern recognition principles

The present work gives an account of basic principles and available techniques for the analysis and design of pattern processing and recognition systems. Areas covered include decision functions, pattern classification by distance functions, pattern classification by likelihood functions, the perceptron and the potential function approaches to trainable pattern classifiers, statistical approach to trainable classifiers, pattern preprocessing and feature selection, and syntactic pattern recognition.

Tou, J. T.↗
NASA NTRS1975

A boundary finding algorithm and its applications

An algorithm for locating gray level and/or texture edges in digitized pictures is presented. The algorithm is based on the concept of hypothesis testing. The digitized picture is first subdivided into subsets of picture elements, e.g., 2 x 2 arrays. The algorithm then compares the first- and second-order statistics of adjacent subsets; adjacent subsets having similar first- and/or second-order statistics are merged into blobs. By continuing this process, the entire picture is segmented into blobs such that the picture elements within each blob have similar characteristics. The boundaries between the blobs comprise the boundaries. The algorithm always generates closed boundaries. The algorithm was developed for multispectral imagery of the earth's surface. Application of this algorithm to various image processing techniques such as efficient coding, information extraction (terrain classification), and pattern recognition (feature selection) are included.

Gupta, J. N.↗
NASA NTRS1977

Image data-processing system for solar astronomy

The paper describes an image data processing system (IDAPS), its hardware/software configuration, and interactive and batch modes of operation for the analysis of the Skylab/Apollo Telescope Mount S056 X-Ray Telescope experiment data. Interactive IDAPS is primarily designed to provide on-line interactive user control of image processing operations for image familiarization, sequence and parameter optimization, and selective feature extraction and analysis. Batch IDAPS follows the normal conventions of card control and data input and output, and is best suited where the desired parameters and sequence of operations are known and when long image-processing times are required. Particular attention is given to the way in which this system has been used in solar astronomy and other investigations. Some recent results obtained by means of IDAPS are presented.

Wilson, R. M.↗
NASA NTRS1977

Computer-aided classification for remote sensing in agriculture and forestry in Northern Italy

A set of results concerning the processing and analysis of data from LANDSAT satellite and airborne scanner is presented. The possibility of performing inventories of irrigated crops-rice, planted groves-poplars, and natural forests in the mountians-beeches and chestnuts, is investigated in the Po valley and in an alphine site of Northern Italy. Accuracies around 95% or better, 70% and 60% respectively are achieved by using LANDSAT data and supervised classification. Discrimination of rice varieties is proved with 8 channels data from airborne scanner, processed after correction of the atmospheric effect due to the scanning angle, with and without linear feature selection of the data. The accuracies achieved range from 65% to more than 80%. The best results are obtained with the maximum likelihood classifier for normal parameters but rather close results are derived by using a modified version of the weighted euclidian distance between points, with consequent decrease in computing time around a factor 3.

Dejace, J.↗
NASA NTRS1978

Automatic classification of reforested Pinus SPP and Eucalyptus SPP in Mogi-Guacu, SP, Brazil, using LANDSAT data

The author has identified the following significant results. Single date LANDSAT CCTs were processed, by Image-100 to classify Pinus and Eucalyptus species and their age groups. The study area Mogi-Guagu was located in the humid subtropical climate zone of Sao Paulo. The study was divided into ten preliminary classes and featured selection algorithms were used to calculate Bhattacharyya distance between all possible pairs of these classes in the four available channels. Classes having B-distance values less than 1.30 were grouped in four classes: (1) class PE - P. elliottii, (2) class P0 - Pinus species other than P. elliotii, (3) class EY - Eucalyptus spp. under two years, and (4) class E0 - Eucalyptus spp. more than two years old. The percentages of correct classification ranged from 70.9% to 94.12%. Comparisons of acreage estimated from the Image-100 with ground truth data showed agreement. The Image-100 percent recognition values for the above four classes were 91.62%, 87.80%, 89.89%, and 103.30%, respectively.

Dejesusparada, N.↗
NASA NTRS1979

Learning with imperfectly labeled patterns

The problem of learning in pattern recognition using imperfectly labeled patterns is considered. The performance of the Bayes and nearest neighbor classifiers with imperfect labels is discussed using a probabilistic model for the mislabeling of the training patterns. Schemes for training the classifier using both parametric and non parametric techniques are presented. Methods for the correction of imperfect labels were developed. To gain an understanding of the learning process, expressions are derived for success probability as a function of training time for a one dimensional increment error correction classifier with imperfect labels. Feature selection with imperfectly labeled patterns is described.

Chittineni, C. B.↗
NASA NTRS1979

LACIE/ERIPS software system summary

The Earth resources interactive processing system (ERIPS) supports LACIE by classifying LANDSAT sensed data on the basis of the statistical similarity to those portions which were identified by analysts. The development and capabilities of the ERIPS software system are described with emphasis on (1) system requirements; (2) LACIE/ERIPS hardware; (3) system functions; (4) pattern recognition concept; and (5) LACIE/ERIPS data bases. Algorithms used in LACIE/ERIPS for statistics, divergence, feature selection, classification, registration, adaptive clustering, iterative clustering, clustering report functions, Sun angle correction, mean level adjustment, and bias correction are appended.

Johnson, C. L.↗