Similarity Grouping of Paintings by Distance Measure and Self Organizing Map
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
A geographical approach to self-organizing maps algorithm applied to image segmentation
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
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To deal with the huge volume of information provided by remote sensing satellites, which produce images used for agriculture monitoring, urban planning, deforestation detection and so on, several algorithms for image classification have been proposed in the literature. This article compares two approaches, called Expectation-Maximization (EM) and Self-Organizing Maps (SOM) applied to unsupervised image classification, i.e. data clustering without direct intervention of specialist guidance. Remote sensing images are used to test both algorithms, and results are shown concerning visual quality, matching rate and processing time.