| Título |
Semantic Decomposition of LandSat TM Image |
| Tipo |
Revista |
| Sub-tipo |
JCR |
| Descripción |
Lecture Notes in Computer Science; 10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems |
| Resumen |
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. |
| Observaciones |
Knowledge-Based Intelligent Information and Engineering Systems; (Including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) KES 2006; Code 68477; LNAI - I, |
| Lugar |
Bournemouth |
| País |
Reino Unido |
| No. de páginas |
550-558 |
| Vol. / Cap. |
4251 |
| Inicio |
2006-10-09 |
| Fin |
2006-10-11 |
| ISBN/ISSN |
978-3-540-46535-5 |
