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Multisource data fusion with multiple self-organizing maps
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DOI：10.1109/36.763298.png)
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This paper presents a self-organizing neural network approach, known as multiple self-organizing maps (MSOM's), to multisource data fusion and compound classification, We use the Kohonen SOM as a building block to set up a design framework for a range of classifiers. We 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 ran be adequately addressed. Experiments with a bitemporal data set show the effectiveness of our approach.
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8.6
论文数:
2.1W
被引数:
10.7W
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