Multisource data fusion with multiple self-organizing maps

Abstract

This paper presents a self-organizing neural network approach, known as multiple self-organizing maps (MSOMs), to multisource data fusion and compound classification. The authors use the Kohonen SOM as a building block to set up a design framework for a range of classifiers. They demonstrate that the MSOM is suitable for multisource fusion, where the issues of high dimensionality, complex characteristics and disparity, and joint exploration of spatiality and temporality of mixed data can be adequately addressed. Experiments with a bitemporal data set show the effectiveness of their approach.