In this paper, a new approach to Mediterranean Water Eddy border detection is proposed. Kohonen self-organizing maps (SOM) are used as data mining tools to cluster image pixels through an unsupervised process. The clusters are visualized on the SOM internal map. From the visualization, the borders can be detected through an interactive way. As a result, interesting patterns are visible on the images. The proposed SOM approach is tested on Atlantic Ocean satellite data and compared with conventional gradient edge detectors. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Marques, N. C., & Chen, N. (2003). Border detection on remote sensing satellite data using self-organizing maps. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2902, 294–307. https://doi.org/10.1007/978-3-540-24580-3_35
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