This Article 
 Bibliographic References 
 Add to: 
Classification by Thresholding
January 1983 (vol. 5 no. 1)
pp. 48-54
Alan H. Feiveson, NASA Johnson Space Center, Houston, TX 77058.
A procedure is given which substantially reduces the processing time needed to perform maximum likelihood classification on large data sets. The given method uses a set of fixed thresholds which, if exceeded by one probability density function, makes it unnecessary to evaluate a competing density function. Proofs are given of the existence and optimality of these thresholds for the class of continuous, unimodal, and quasi-concave density functions (which includes the multivariate normal), and a method for computing the thresholds is provided for the specifilc case of multivariate normal densities. An example with remote sensing data consisting of some 20 000 observations of four-dimensional data from nine ground-cover classes shows that by using thresholds, one could cut the processing time almost in half.
Alan H. Feiveson, "Classification by Thresholding," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 5, no. 1, pp. 48-54, Jan. 1983, doi:10.1109/TPAMI.1983.4767343
Usage of this product signifies your acceptance of the Terms of Use.