The Kohonen map for credal fusion of heterogeneous data

Fusion of optical and radar remote sensing data is an important and challenging task for many applications such as multisource classification, due to the highly heterogeneous nature of the information they contain. The aim of this paper is to propose a novel strategy for multisensor optical-radar data classification in the evidential framework, which is well known for its ability to deal with imperfect information of each sensor and for its ability to exploit the redundancies and the complementarities between fused data and between sensors. A major contribution of our work is the use of Kohonen´s map to model the input evidences of radar sensor since the optical sensor was addressed in our previous works. Also a specific design is presented here for fusing those heterogeneous data. First experiments show the effectiveness of the proposed approach.