Interpretation of Multisensor Remote Sensing Images: Multiapproach Fusion of Uncertain Information

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.

I. Introduction

Interpretation of multisensor remote sensing images is in full evolution, allowing the generation of up-to-date land cover information. Due to the development of various image sensors (visible, infrared, synthetic aperture radar (SAR), etc.), interpretation of the scene can be done through the fusion of data provided by these sensors (multisensor fusion) [1]. However, the interpretation process is generally characterized by numerous types of imperfection [2]. Therefore, the problem of managing imprecise and uncertain data is growing; in fact, imprecision and uncertainty are becoming more complex in multisensor fusion [1]. Interpretation systems should be able to deal with this kind of information [3]. We have to choose between two possible solutions: either representing uncertainty and imprecision or working with uncertain and imprecise information. In this paper, we present the three most commonly used mathematical frameworks that try to overcome the problem of imperfection accompanying the image interpretation process, i.e., the probability, possibility, and evidence theories [1]. Each of these three models has its own operations to combine and process information and is more appropriate for a specific situation of given information and for a particular type of imperfection [4]. The most commonly used image interpretation systems do not take into account the imperfection accompanying these images, and the few systems that do use only one theory with very restricted parameters [3].