Abstract :
Modern imaging sensors, especially those aboard satellites, continuously deliver enormous amounts of data. The widespread of meter resolution images, is not only exploding the volumes of acquired data but also brings a new dimension in the image detail, thus growing the information content. These represent typical cases, where users need automated tools to discover, explore and explain the contents of large image databases. There is a strong need to build up applications that help the user in image interpretation task, applications that permit to query the archives in content based mode, without having to know all the information contained in the images at signal level. We propose in this article, a synergy between stochastic modelling, knowledge discovery, and semantic representation. To do that, we associate semantic labels to a combination of primitive image features. The user-defined semantic image content interpretation is linked with Bayesian networks to a completely unsupervised classification. This new paradigm for the interaction with EO archives can provide several applications for users coming from different domains, as change detection, agricultural field classification, environment monitoring, atmosphere effects or urbanization.
Keywords :
belief networks; data mining; geophysical signal processing; image classification; image representation; information retrieval; semantic networks; visual databases; Bayesian networks; Earth Observation archives; knowledge discovery; multiclass image content retrieval; primitive image features; semantic labels; semantic representation; spaceborne imaging sensors; stochastic modelling; unsupervised classification; user defined semantic image content interpretation; Bayesian methods; Content based retrieval; Electrooptic effects; Image databases; Image resolution; Image retrieval; Image sensors; Monitoring; Satellites; Stochastic processes;
