This paper reviews the results of multidimensional image analysis and visualization studies using n-dimensional Probability Density Functions (nPDF) algorithm. The nPDF technique is an approach to the visualization and analysis of multispectral data and overcomes many of the problems inherent to traditional classifiers that rely on purely statistical approaches to describe data and class (or training field) distribution. A graphical method, in conjunction with statistical techniques, has the advantage of providing a multidimensional data distribution and may be used for supervised and unsupervised classifications. The approach is particularly useful for comparing training data with the spectral classes present in the entire data set. Compared to the conventional statistical classifiers, the nPDF procedure is extremely fast and user-interactive. The approach relies on data visualization techniques and displays data and class distribution graphically. In this paper, a review of the theory and applications of the technique are given. The data processing procedure for supervised and unsupervised classifications using the interactive nPDF method and comparison of the nPDF technique with the traditional algorithms are also discussed.
Haluk Cetin, Haluk Cetin,
"Visualization and interactive analysis of multidimensional image data", Proc. SPIE 2656, Visual Data Exploration and Analysis III, (8 March 1996); doi: 10.1117/12.234667; https://doi.org/10.1117/12.234667