Spatio-Temporal Modeling for Knowledge Discovery in Satellite Image Databases

Knowledge discovery from satellite images in spatio-temporal context remains one of the major challenges in the remote sensing field. It is, always, difficult for a user to manually extract useful information especially when processing a large collection of satellite images. Thus, we need to use automatic knowledge discovery in order to develop intelligent image interpretation systems. In this paper, we present a high-level approach for modeling spatio-temporal knowledge from satellite images. We also propose to use a multi-approach segmentation involving several segmentation methods which help improving images modeling and interpretation. The experiments, made on LANDSAT scenes, show that our approach outperforms classical methods in image segmentation and are able to predict spatio-temporal changes of satellite images. MOTS-CL

• Type: Journal
• Year: 2010
• Where: CORIA
• Authors: Wadii Boulila, Imed Riadh Farah, Karim Saheb Ettabaa, Basel Solaiman, Henda Ben Ghézala
• Keywords: CORIA 2010, Information Technology, Knowledge Discovery, Remote Sensing Field, Satellite Images