Interpretation of multisensor remote sensing images : multiapproach fusion of uncertain information

• Published in: IEEE transactions on geoscience and remote sensing Vol. 46; no. 12; pp. 4142 - 4152
• Main Authors: Farah, Imed Riadh, Boulila, Wadii, Saheb Ettabaa, Karim, Ben Ahmed, Mohamed, Solaiman, Basel
• Format: Journal Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers 01-12-2008
• Subjects: Engineering Sciences; Signal and Image processing
• ISSN: 0196-2892
• DOI: 10.1109/TGRS.2008.2000817

Land cover interpretation using multisensor remote sensing images is an important task that allows the extraction of information that is useful for several applications. However, satellite images are usually characterized by several types of imperfection, such as uncertainty, imprecision, and ignorance. Using additional sensors can help improve the image interpretation process and decrease the associated imperfections. Fusion methods such as the probability, possibility, and evidence methods can be used to combine information coming from these sensors. An extensive literature has accumulated during the last decade to resolve the issue of choosing the best fusion method, particularly for satellite images. In this paper, we present a semiautomatic approach based on case-based reasoning (CBR) and rule-based reasoning, allowing intelligent fusion method retrieval. This approach takes into account the advantage of data stored in the case base, allowing a more efficient processing and a decrease in image imperfections. The proposed approach incorporates three modules. The first is a learning module based on evaluating three fusion methods (probability, possibility, and evidence) applied to the given satellite images. The second looks for the best fusion method using CBR. The last is devoted to the fusion of multisensor images using the method retrieved by CBR. We validate our approach on a set of optical images coming from the Satellite Pour l'Observation de la Terre 4 and radar images coming from European Remote Sensing Satellite 2 (ERS-2) representing a central Tunisian region.