Coastal image interpretation using background knowledge and semantics

Germain Forestier (1, 2) , Cédric Wemmert (3) , Anne Puissant (4)

1 Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar
2 IRIMAS - Institut de Recherche en Informatique Mathématiques Automatique Signal - IRIMAS - UR 7499
3 UNISTRA - Université de Strasbourg
4 LIVE - Laboratoire Image, Ville, Environnement

In this article, we present a framework to model and to use knowledge provided by experts for remote sensing image interpretation of coastal area. The goal of this approach is to associate semantic to regions issued from the segmentation of an image. The idea is to start with a raw description of the knowledge given by the expert on the different thematic object classes present in the image. This knowledge is then decomposed and formalized to be usable during the classification process. A first interpretation of the image is computed through an ontology with spectral information about the classes. Then, a set of Knowledge Functions (KFs) are defined according to the description of the expert's knowledge. These KFs are then used to check the consistency of the spectral interpretation and to detect potentially mislabeled regions. The interpretation of these regions is revised in an iterative process to produce a more accurate final result. Experiments on remote sensing images of a coastal zone of Normandy, France are presented to show the relevance of the method.

Mots clés: knowledge-based image interpretation; ontology; coastal management

Domaines: Intelligence artificielle [cs.AI]

Germain Forestier, Cédric Wemmert, Anne Puissant. Coastal image interpretation using background knowledge and semantics. Computers & Geosciences, 2013, 54, pp.88 - 96. ⟨10.1016/j.cageo.2012.11.023⟩. ⟨hal-01875858⟩