Fuzzy Spatio-Spectro-Temporal Ontology for Remote Sensing Image Annotation and Interpretation: Application to Natural Risks Assessment

Abstract

This research deals with semantic interpretation of Remote Sensing Images (RSIs) using ontologies which are considered as one of the main challenging methods for modeling high-level knowledge, and reducing the semantic gap between low-level features and high-level semantics of an image. In this paper, we propose a new ontology which allows the annotation as well as the interpretation of RSI with respect to natural risks, while taking into account uncertainty of data, object dynamics in natural scenes, and specificities of sensors. In addition, using this ontology, we propose a methodology to (i) annotate the semantic content of RSI, and (ii) deduce the susceptibility of the land cover to natural phenomena such as erosion, floods, and fires, using case-based reasoning supported by the ontology. This work is tested using LANDSAT and SPOT images of the region of Kef which is situated in the north-west of Tunisia. Results are rather promising.