filename	: -
title		: Visualization of multidimensional image data sets using a neural network
author		: M. H. Gross, F. Seibert
type		: inproceedings
organization	: Institute for Information Systems, ETH Zürich
month		: 
year		: 1993
booktitle	: The Visual Computer
keywords	: Visualization of multidimensional data - Cluster analysis - Vector quantization - Topological ordering - Neural networks - Remote sensing - Image classification - Environmental protection
language	: English
pages		: 145-159
abstract:
This paper describes the application of self-organizing neural networks on the analysis and visualization of multidimensional data sets. First, a mathematical description of cluster analysis, dimensionality reduction, and topological ordering is given taking these methods as problems of discrete optimization. Then, the Kohonen map is introduced, that orders its neurons according to topological features of the data sets to be trained with. For this reason, it can also be called a topology-preserving feature map.
In order to visualize the results obtained during the self-organization process, the standard map has been extended to a three-dimensional cube of neurons, where each neuron represents a discrete entity in the red green blue color space (RGB). According to the ordering properties of the network neighbored neurons and thus similr colors refer to data vectors with similar features.
The application of this technique on multidimensional Landsat-TM remotely sensed image data, namely, the analysis of the burning oil fields in Kuwait, demonstrates the capabilities of the introduced method. Moreover it can be used to solve general visualization problems of data mapping into a lower dimensional representation.