No abstract

No abstract

Abstract-The International Charter1 "Space and Major Disasters" is regularly activated during a catastrophic event and offers rescue team damage maps. Most of these maps are built by means of satellite image manual processing, which is often complex and demanding in terms of time and energy. Automatic processing offers prompt treatment; nevertheless it usually presents a semantic gap handicap. The exploitation of ontologies to bridge the semantic gap has been widely recommended due to their quality of knowledge representation, expression, and discovery. In this work, we present an ontology-based semantic hierarchical classification method to undertake this problem. Ontology components are translated to image-based parameters and used to assist the classification process with two levels and 12 embedded classes. The region of interest is selected from the first level, and exhaustively analyzed and classified at the second level. The 2010 Haiti earthquake was selected as study area for this work. Experiments were performed using very high resolution multi-temporal QuickBird imagery and eCognition software.

No abstract

No abstract