Accession Number : ADA300331

Title :   A Comparison of the Performance of Non-Parametric Classifiers with Gaussian Maximum Likelihood for the Classification of Multispectral Remotely Sensed Data.

Descriptive Note : Master's thesis,

Corporate Author : AIR FORCE ACADEMY COLORADO SPRINGS CO

Personal Author(s) : Nessmiller, Steven W.

PDF Url : ADA300331

Report Date : 20 OCT 1995

Pagination or Media Count : 256

Abstract : This study compares the performance of two non-parametric classifiers and Gaussian Maximum Likelihood (GML) for the classification of LANDSAT TM 30-meter resolution six-band data. The mathematical assumptions made in developing GML are valid if the pixels that constitute the training classes are normally distributed. Since it requires a model of the data, GML is termed a "parametric" classifier. Of current interest are new classification methodologies that make no assumptions about the statistical distribution of the pixels in the training class; these approaches are termed non-parametric' classifiers. This study will compare the n-Dimensional Probability Density Function (nPDF) essentially a projection technique that reduces data dimensionality, and an advanced neural network that utilizes fuzzy-set mathematics, the Fuzzy ARTMAP, to the traditional GML approach to image classification. The different approaches will be compared for statistical classification accuracy and computational efficiency. (AN)

Descriptors :   *IMAGE PROCESSING, *NEURAL NETS, *NONPARAMETRIC STATISTICS, MATHEMATICAL MODELS, ALGORITHMS, OPTIMIZATION, COMPUTATIONS, DATA MANAGEMENT, MAXIMUM LIKELIHOOD ESTIMATION, MATRICES(MATHEMATICS), COMPARISON, RESOLUTION, ANALYSIS OF VARIANCE, ACCURACY, LEARNING MACHINES, THESES, INPUT OUTPUT PROCESSING, PARALLEL PROCESSING, PROBABILITY DENSITY FUNCTIONS, CLASSIFICATION, PATTERN RECOGNITION, COMPUTER VISION, PIXELS, REMOTE DETECTION, MULTISPECTRAL, IMAGE REGISTRATION, SYNCHRONOUS SATELLITES, NORMAL DISTRIBUTION, STATISTICAL DECISION THEORY.

Subject Categories : Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE