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.
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Groß, M.H., Seibert, F. Visualization of multidimensional image data sets using a neural network. The Visual Computer 10, 145–159 (1993). https://doi.org/10.1007/BF01900904
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DOI: https://doi.org/10.1007/BF01900904