Multisensor data fusion using fuzzy concepts: Application to land-cover classification using ERS-1/JERS-1 SAR composites

In this study, a fuzzy-based multisensor data fusion classifier is de developed and applied to land cover classification using ERS-1/JERS-1 SAR composites. This classifier aims at the integration of multisensor and contextual information in a single and a homogeneous framework. Initial fuzzy membership maps (FMM's) to different thematic classes are first calculated using classes and sensors a priori knowledge. These FMM's are then iteratively updated using spatial contextual information. A classification rule is associated to different iterations. This classifier has the following advantages: first, due to the use of fuzzy concepts, it has the flexibility of integrating multisensor/contextual and a priori information. Second, the classification results consist of thematic as well as confidence maps. The confidence map (a classification certainty map representing the degree of certainty in the thematic map) constitutes an important issue in order to evaluate the classification process complexity and the validity of the assumptions. The application of this classifier using ERS1/JERS-1 SAR composites is shown to be promising.