Abstract
In this paper, we propose a semantic supervised clustering approach to classify multispectral information in geo-images. We use the Maximum Likelihood Method to generate the clustering. In addition, we complement the analysis applying spatial semantics to determine the training sites and to improve the classification. The approach considers the a priori knowledge of the multispectral geo-image to define the classes related to the geographic environment. In this case the spatial semantics is defined by the spatial properties, functions and relations that involve the geo-image. By using these characteristics, it is possible to determine the training data sites with a priori knowledge. This method attempts to improve the supervised clustering, adding the intrinsic semantics of the geo-images to determine the classes that involve the analysis with more precision.
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© 2006 Springer-Verlag Berlin Heidelberg
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Torres, M., Guzmán, G., Quintero, R., Moreno, M., Levachkine, S. (2006). Semantic Decomposition of LandSat TM Image. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_67
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DOI: https://doi.org/10.1007/11892960_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46535-5
Online ISBN: 978-3-540-46536-2
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