Abstract: A new method for image clustering with density maps derived from Self-Organizing Maps (SOM) is proposed together with a clarification of learning processes during a construction of clusters. Simulation studies and the experiments with remote sensing satellite derived imagery data are conducted. It is found that the proposed SOM based image clustering method shows much better clustered result for both simulation and real satellite imagery data. It is also found that the separability among clusters of the proposed method is 16% longer than the existing k-mean clustering. It is also found that the separability among clusters of the proposed method is 16% longer than the existing k-mean clustering. In accordance with the experimental results with Landsat-5 TM image, it takes more than 20000 of iteration for convergence of the SOM learning processes.

Kohei Arai, “Image Clustering Method Based on Self Organization Mapping: SOM Derived Density Maps and Its Application for Landsat Thematic Mapper Image ” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 2(5), 2013. http://dx.doi.org/10.14569/IJARAI.2013.020504

@article{Arai2013,
title = {Image Clustering Method Based on Self Organization Mapping: SOM Derived Density Maps and Its Application for Landsat Thematic Mapper Image },
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2013.020504},
url = {http://dx.doi.org/10.14569/IJARAI.2013.020504},
year = {2013},
publisher = {The Science and Information Organization},
volume = {2},
number = {5},
author = {Kohei Arai}
}