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General Algorithm for the Semantic Decomposition of Geo-Image

Keywords: image processing and computer vision, scene analysis, object recognition.

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the thesis presents an object oriented methodology for the semantic extraction of a geo-image which is defined by a set of natural language labels. the approach is composed of two main stages: analysis and synthesis. the analysis stage detects the main geographic components of a geo-image by means of the color quantification, geometry and topology of the geospatial objects. the result of this stage is a set of geo-images with intensities that are approximately uniform. the synthesis stage extracts the main geographic objects that have been identified and a labeling process in two levels (general and specialized), which is equivalent to consider both local and global information of a geo-image. the aim of the general labeling process is to associate a label of the adequate thematic to each region, taking into account the rgb characteristics of the image. in order to specialize each geographic object, we have proposed a specialization algorithm that considers geometric and topologic relations among them, represented in geographic application domain ontology. the obtained set of labels describes the geo-image semantics.


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