Tor Vergata GeoInformation Doctorate

Information Mining in Remote Sensing Image
Archives: System Concepts

Mihai Datcu, Herbert Daschiel, Andrea Pelizzari, Marco Quartulli, Annalisa Galoppo, Andrea Colapicchioni,
Marco Pastori, Klaus Seidel, Pier Giorgio Marchetti, and Sergio D’Elia


In this paper, we demonstrate the concepts of a prototype of a knowledge-driven content-based information mining system produced to manage and explore large volumes of remote sensing image data. The system consists of a computationally intensive offline part and an online interface. The offline part aims at the extraction of primitive image features, their compression, and data reduction, the generation of a completely unsupervised image content-index, and the ingestion of the catalogue entry in the database management system. Then, the user’s interests—semantic interpretations of the image content—are linked with Bayesian networks to the content-index. Since this calculation is only based on a few training samples, the link can be computed online, and the complete image archive can be searched for images that contain the defined cover type. Practical applications exemplified with different remote sensing datasets show the potential of the system.

Index Terms - Content-based image retrieval (CBIR), image information mining, information extraction, statistical learning.


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