title: Neural maps in remote sensing image analysis.
abstract: We study the application of self-organizing maps (SOMs) for the analyses of remote sensing spectral images. Advanced airborne and satellite-based imaging spectrometers produce very high-dimensional spectral signatures that provide key information to many scientific investigations about the surface and atmosphere of Earth and other planets. These new, sophisticated data demand new and advanced approaches to cluster detection, visualization, and supervised classification. In this article we concentrate on the issue of faithful topological mapping in order to avoid false interpretations of cluster maps created by an SOM. We describe several new extensions of the standard SOM, developed in the past few years: the growing SOM, magnification control, and generalized relevance learning vector quantization, and demonstrate their effect on both low-dimensional traditional multi-spectral imagery and approximately 200-dimensional hyperspectral imagery.
authors.affiliation: Klinik für Psychotherapie, Universität Leipzig, Karl-Tauchnitz-Str. 25, 04107, Leipzig, Germany. villmann@informatik.uni-leipzig.de
authors.forename: Thomas
authors.initials: T
authors.lastname: Villmann
authors.forename: Erzsébet
authors.initials: E
authors.lastname: Merényi
authors.forename: Barbara
authors.initials: B
authors.lastname: Hammer
mesh_terms: D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer