Semantic Supervised Clustering to Land Classification in Geo-Images
| Autor: | Serguei Levachkine, Rolando Quintero, Miguel Torres, Giovanni Guzmán, Marco Moreno |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
Geographic information system
Fuzzy clustering Training set Contextual image classification business.industry Computer science Semantic analysis (machine learning) Supervised learning Pattern recognition Machine learning computer.software_genre ComputingMethodologies_PATTERNRECOGNITION Artificial intelligence business Cluster analysis computer |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783540288961 KES (3) |
| DOI: | 10.1007/11553939_36 |
| Popis: | In this paper, we propose a semantic supervised clustering approach to classify lands in geo-images. We use the Maximum Likelihood Method to generate the clustering. In addition, we complement the analysis applying spatial semantics to improve the classification. The approach considers the a priori knowledge of the multispectral image to define the training sites (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 training sites that involve the analysis with more precision. |
| Databáze: | OpenAIRE |
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