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Guseman, L. F., Jr.

Publications and source records attributed to Guseman, L. F., Jr..

29 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

Image analysis library software development

The Image Analysis Library consists of a collection of general purpose mathematical/statistical routines and special purpose data analysis/pattern recognition routines basic to the development of image analysis techniques for support of current and future Earth Resources Programs. Work was done to provide a collection of computer routines and associated documentation which form a part of the Image Analysis Library.

Guseman, L. F., Jr.↗
NASA NTRS1976

Applications of feature selection

The use of satellite-acquired (LANDSAT) multispectral scanner (MSS) data to conduct an inventory of some crop of economic interest such as wheat over a large geographical area is considered in relation to the development of accurate and efficient algorithms for data classification. The dimension of the measurement space and the computational load for a classification algorithm is increased by the use of multitemporal measurements. Feature selection/combination techniques used to reduce the dimensionality of the problem are described.

Guseman, L. F., Jr.↗
NASA NTRS1975

A method for estimating proportions

A proportion estimation procedure is presented which requires only on set of ground truth data for determining the error matrix. The error matrix is then used to determine an unbiased estimate. The error matrix is shown to be directly related to the probability of misclassifications, and is more diagonally dominant with the increase in the number of passes used.

Guseman, L. F., Jr.↗
NASA NTRS1975

Optimal selection of passes

Preliminary numerical results obtained from the application of a linear feature selection technique to the determination of combinations of passes which best discriminate between a given set of crops in a given area of interest, are reported. The results obtained are not purported to hold in a general situation, but only for the given set of crops and the given, but unknown, levels of several factors-such as soil type, and fertilizer practice, holding in the area of interest. However, by identifying the various factors affecting the spectral signatures, and by formulating a regression model one could use the feature selection technique to determine the regression coefficients for predicting optimal passes for a given set of crops. Another use of the feature selection technique as applied to multiple pass registered data is the generation of enhanced grey scale displays by using a single linear combination of all channels of all designated passes as opposed to a single channel within a single pass.

Guseman, L. F., Jr.↗
NASA NTRS1975

LFSPMC: Linear feature selection program using the probability of misclassification

The computational procedure and associated computer program for a linear feature selection technique are presented. The technique assumes that: a finite number, m, of classes exists; each class is described by an n-dimensional multivariate normal density function of its measurement vectors; the mean vector and covariance matrix for each density function are known (or can be estimated); and the a priori probability for each class is known. The technique produces a single linear combination of the original measurements which minimizes the one-dimensional probability of misclassification defined by the transformed densities.

Guseman, L. F., Jr.↗
NASA NTRS1975

Image 100 procedures manual development: Applications system library definition and Image 100 software definition

An outline for an Image 100 procedures manual for Earth Resources Program image analysis was developed which sets forth guidelines that provide a basis for the preparation and updating of an Image 100 Procedures Manual. The scope of the outline was limited to definition of general features of a procedures manual together with special features of an interactive system. Computer programs were identified which should be implemented as part of an applications oriented library for the system.

Guseman, L. F., Jr.↗
NASA NTRS1973

On minimizing the probability of misclassification for linear feature selection

The use of techniques for feature selection permits treatment of classification problems in spaces of reduced dimensions. A method is considered of linear feature selection for n-dimensional observation vectors which belong to one of two populations, where each population is described by a known multivariate normal density function. More specifically, the problem of finding a 1xn transformation matrix B for which the probability of misclassification with respect to the one-dimensional transformed density functions was minimized was considered. Theoretical results are presented which give rise to a numerically tractable expression for the variation in the probability of misclassification with respect to B. Using this expression a computational procedure is discussed for obtaining a B which minimizes the probability of misclassification. Preliminary numerical results are discussed.

Guseman, L. F., Jr.↗