We present a new classification method using a deformable template model to separate natural objects from man made objects in an image given by a high resolution sonar. A prior knowledge of the manufactured object shadow shape is described by a prototype template and a set of admissible linear transformations to take into account the shape variability. Then, the classification problem is defined as a two step process; firstly the detection problem of a region of interest in the input image is stated in a Bayesian framework and is posed as an equivalent energy minimization problem of an objective function: in this paper, this energy minimization problem is solved by using a hybrid genetic algorithm (GA). Secondly, the value of this function at convergence allows one to determine the presence of the desired object in the sonar image. This method has been successfully tested on real and synthetic sonar images, yielding very promising results.
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