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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Torres, M., Guzmán, G., Quintero, R., Moreno, M., & Levachkine, S. (2006). Semantic decomposition of LandSat TM image. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4251 LNAI-I, pp. 550–558). Springer Verlag. https://doi.org/10.1007/11892960_67
Mendeley helps you to discover research relevant for your work.