Bayesian inference and optimization strategies for some detection and classification problems in sonar imagery

In this paper, we investigate the use of the Bayesian inference for some detection and classification problems of great importance in sonar imagery. More precisely this paper is concerned with the segmentation of sonar image, the classification of object lying on the sea-bottom and the classification of sea-floor. These aforementioned classification tasks are based on the identification of the detected cast shadows which correspond to a lack of acoustic reverberation behind the different natural or man-made objects lying on the sea-floor. The adopted Bayesian approach allows to model efficiently all the available prior information, for each detection and;classification task under concern yielding a cost minimization problem. To this end, we associate to each Bayesian statistical modeling, a specific optimization strategy well suited to the global energy function to be minimized. These segmentation and classification schemes can be used separately for a specific application or can lead to an original Bayesian processing chain for the automatic classification of objects lying on the sea-floors. The efficiency and robustness of this unsupervised processing chain has been tested and demonstrated on a great number of real and synthetic sonar images.