Probabilistic cluster labeling of imagery data
(Previously announced in STAR as N82-22590)
Minter, T. C.↗
Engineering topics
Minter, T. C.
Publications and source records attributed to Minter, T. C..
Methods of extending crop signatures from one area to another
Efforts to develop a technology for signature extension during LACIE phases 1 and 2 are described. A number of haze and Sun angle correction procedures were developed and tested. These included the ROOSTER and OSCAR cluster-matching algorithms and their modifications, the MLEST and UHMLE maximum likelihood estimation procedures, and the ATCOR procedure. All these algorithms were tested on simulated data and consecutive-day LANDSAT imagery. The ATCOR, OSCAR, and MLEST algorithms were also tested for their capability to geographically extend signatures using LANDSAT imagery.
Minter, T. C.↗
Selecting class weights to minimize classification bias in acreage estimation
Preliminary results of experiments being performed to select optimal class weights for use with the maximum likelihood classifier in acreage estimation using remote sensor imagery are presented. These weights will be optimal in the sense that the bias will be minimized in the proportion estimate obtained from the classification results by sample counting. The procedure was tested using Landsat MSS data from an 8 by 9.6 km area of ground truth in Finney County, Kansas.
Belcher, W. M.↗
Bayes estimation on parameters of the single-class classifier
Normal procedures used for designing a Bayes classifier to classify wheat as the major crop of interest require not only training samples of wheat but also those of nonwheat. Therefore, ground truth must be available for the class of interest plus all confusion classes. The single-class Bayes classifier classifies data into the class of interest or the class 'other' but requires training samples only from the class of interest. This paper will present a procedure for Bayes estimation on the mean vector, covariance matrix, and a priori probability of the single-class classifier using labeled samples from the class of interest and unlabeled samples drawn from the mixture density function.
Lin, G. C.↗
Single-class classification
Often, when classifying multispectral data, only one class or crop is of interest, such as wheat in the Large Area Crop Inventory Experiment (LACIE). Usual procedures for designing a Bayes classifier require that labeled training samples and therefore ground truth be available for the 'class of interest' plus all confusion classes defined by the multispectral data. This paper will consider the problem of designing a two-class Bayes classifier which will classify data into the 'class of interest' or the 'other' classes but will require only labeled training samples from the 'class of interest' to design the classifier. Thus, this classifier minimizes the need for ground truth. For these reasons, the classifier is referred to as a single-class classifier. A procedure for evaluating the overall performance of the single-class classifier in terms of the probability of error will be discussed.
Minter, T. C.↗
LACIE ADP/PI joint case study: ADP analysis guidelines
The procedure is described which was used to train automatic data processing (ADP) analysts to process ERTS 1 imagery from a 5 nm by 6 nm area in Delisle, Canada, and to estimate wheat acreage using training fields provided by photointerpreters. The exercise also served to evaluate and test current large area crop inventory experiment (LACIE) procedures.
Minter, T. C.↗
Maximum likelihood classification by thresholding
The standard maximum-likelihood classifier is reformulated so that, in most cases, only a small number of density functions need be computed each time a data point is to be classified. The technique relies upon class thresholds which are obtained at the beginning of the classification process and which remain fixed thereafter. The result of the reformulation is that a significant reduction in classification processing time is obtained while retaining complete consistency with the standard maximum-likelihood classifier.
Minter, T. C.↗