Abstract
The Self-Organizing Map (SOM), a powerful method for clustering and knowledge discovery, has been used effectively for remote sensing spectral images which often have high-dimensional feature vectors (spectra) and many meaningful clusters with varying statistics. However, a learned SOM needs postprocessing to identify the clusters, which is typically done interactively from various visualizations. What aspects of the SOM’s knowledge are presented by a visualization has great importance for cluster capture. We present our recent scheme, CONNvis, which achieves detailed delineation of cluster boundaries by rendering data topology on the SOM lattice. We show discovery through CONNvis clustering in a remote sensing spectral image from the Mars Exploration Rover Spirit.
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Taşdemir, K., Merényi, E. (2008). Cluster Analysis in Remote Sensing Spectral Imagery through Graph Representation and Advanced SOM Visualization. In: Jean-Fran, JF., Berthold, M.R., Horváth, T. (eds) Discovery Science. DS 2008. Lecture Notes in Computer Science(), vol 5255. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88411-8_25
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DOI: https://doi.org/10.1007/978-3-540-88411-8_25
Publisher Name: Springer, Berlin, Heidelberg
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