Engineering topics
Publications and source records attributed to Coberly, W. A..
The theory and applications of the Akaike information criterion (AIC) Mixture model simulations and the application of the AIC to a large portion of a LANDSAT segment are presented.
There are no author-identified significant results in this report.
The overall success of large-scale crop inventories of agricultural regions using Landsat multispectral scanner data is highly dependent upon the labeling of training data by analyst/photointerpreters. The principal analyst tool in labeling training data is a false color infrared composite of Landsat bands 4, 5, and 7. In this paper, this color display is investigated and its influence upon classification errors is partially determined.
There are no author-identified significant results in this report.
There are no author-identified significant results in this report.
An explicit expression for a compression matrix T of smallest possible left dimension K consistent with preserving the n variate normal Bayes assignment of X to a given one of a finite number of populations and the K variate Bayes assignment of TX to that population was developed. The Bayes population assignment of X and TX were shown to be equivalent for a compression matrix T explicitly calculated as a function of the means and covariances of the given populations.
Several candidate signature extensions algorithms were evaluated. Four simulated data sets and seven consecutive day data sets were used. Analysis and evaluation of the UHMLE test and recommendations on changes in the UHMLE algorithm motivated by the test are given for each data set. The criterion for evaluation of each algorithm was overall classification accuracy.
A guide is presented for a computer program which reads multispectral scanner data from a universal format tape and outputs an intermediate data set in card image format for use as an input data set in various data analysis development programs.
A programmer guide for computing maximum likelihood estimates of mixture distributions from an unlabeled sample is presented. A subroutine provides graphic representation of mixture distributions.