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Automatic seabed classification can be achieved using acoustic sensors but methods need to be improved. In order to get better classification reliability, we propose to use complementarity between sidescan sonar images and a digital elevation models (DEM). The new feature is that the sonar (Klein), provides a high resolution sidescan sonar image which pixels are colocated with high resolution interferometric points. After extracting information from each of the two sources, the key point is to fuse them to be able to classify the seabed. We propose to compare three fusion approaches: two signal-level fusion based on multidimensional classification algorithms, and a symbol-level fusion based on the Dempster-Shafer evidence theory. These methods are tested on real sonar data.