A Comparative Study of Topological Feature Maps Versus Conventional Clustering for (Multi-Spectral) Scene Identification in METEOSAT Imagery

Abstract

A connectionist scheme based on auto-adaptive topological feature maps is compared to conventional cluster analysis for (multi-spectral) scene identification in METEOSAT data. The identification of scenes in (multi-spectral) satellite data is equivalent to the assignment of meaningful labels to spatially coherent image regions. Automated scene detection in METEOSAT data is considered as a data reduction problem with the (optimal) preservation of the spatial coherence of scene region information. A self-organising one-dimensional feature map applied to the so-called segment space of the individual METEOSAT channels is shown to be an appropriate tool for mono-spectral scene identification. It is also shown that the presented connectionist approach for auto-adaptive mono-spectral scene identification has two important advantages compared to conventional cluster analysis, i.e. the number of detected regions is never lower than the number of nodes in the feature map and one obtains a contrast enhancement of the image regions. It is argued however that, despite these properties of feature maps, the use of conventional cluster analysis must be preferred for multi-spectral scene identification because multi-dimensional feature maps become far too slow for practical applications and require a detailed statistical analysis of the multi-spectral segment distribution in order to choose the topological dimension of the feature maps.