@inproceedings{d6422803c46a4ba5a08136061928f881,
title = "Semantic decomposition of LandSat TM image",
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.",
author = "Miguel Torres and Giovanni Guzm{\'a}n and Rolando Quintero and Marco Moreno and Serguei Levachkine",
year = "2006",
doi = "10.1007/11892960_67",
language = "Ingl{\'e}s",
isbn = "3540465359",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "550--558",
booktitle = "Knowledge-Based Intelligent Information and Engineering Systems - 10th International Conference, KES 2006, Proceedings",
address = "Alemania",
note = "10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2006 ; Conference date: 09-10-2006 Through 11-10-2006",
}