GEOAIDA applied to SPOT satellite image interpretation

This paper investigates the application of a knowledge-based approach, founded on semantic networks, to the automatic land use mapping assisted by low resolution satellite images. Like the visual photo-interpretation, the automatic image interpretation considers scene and sensors knowledge, delivered by an expert photo-interpreter, as well as additional information about the region like the digital elevation model, the position of the emergent rocks, the mapping of the water bodies and the road-network. By this means, the analysis of a scene can be automatically performed, mimicking the reasoning of the photo-interpreter. The implementation of such proposal employed the GEOAIDA [J. Buckner et al., June 2001] system, a flexible environment for image interpretation developed at the University of Hanover, which exploits semantic networks to structure the domain specific knowledge. In the reported experiments, a multispectral SPOT 3 XS image was analysed resulting were evaluated and compared with a manually made reference map of the investigated scene. The automatically obtained results were evaluated and compared with a manually made reference map of the investigated scene. The experimental results demonstrate the potential of a knowledge-based approach for low resolution satellite images interpretation.