Unsupervised calibrated sonar imaging for seabed observation using hidden Markov random fields

Ronan Fablet (1, 2) , Jean-Marie Augustin (2)

1 SC - Département Signal et Communications
2 IFREMER - Institut Français de Recherche pour l'Exploitation de la Mer

This paper deals with seabed imaging issued from sonar systems. Such imaging systems produce images of backscattering (BS) strength relative to physical seabed characteristics. However, these Bs measurements are not only seabed-related but also dependent on the incident angle. Therefore, to enhance the quality of such seabed imaging systems, we develop an unsupervised approach to compensate for these seabed-related angular dependencies. Our approach combines robust estimation and hidden Markov random fields. Results on real data demonstrate the relevance of our approach to improve seabed observation.

Domaines: Traitement du signal et de l'image [eess.SP]

Ronan Fablet, Jean-Marie Augustin. Unsupervised calibrated sonar imaging for seabed observation using hidden Markov random fields. ICASSP 2006 : IEEE International Conference on Acoustics, Speech and Signal Processing, May 2006, Toulouse, France. pp.693 - 696, ⟨10.1109/ICASSP.2006.1660468⟩. ⟨hal-02344294⟩