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A partially observable Markov decision process (POMDP) is proposed to perform multi-view classification of underwater objects. The model allows one to adaptively determine which additional views of an object would be most beneficial for reducing classification uncertainty. Acquiring additional views is made possible by employing a sonar-equipped autonomous underwater vehicle (AUV) for data collection. The POMDP model is validated using real synthetic aperture sonar (SAS) data collected at sea, with promising results. The approach provides an elegant way to fully exploit multi-view information in a methodical manner.

In April of 2011, FFI led a sea trial near Larvik, Norway on FFIs research vessel the H.U. Sverdrup II with participation by representatives from Canada, United States, and France. One objective of the sea trial was to acquire a data set suitable for examining incoherent and coherent change detection and automated target recognition (ATR) algorithms applied to Synthetic Aperture Sonar (SAS) imagery. The end goal is to produce an automated tool for detecting recently placed objects on the seafloor. To test these algorithms two areas were chosen, one with a comparatively benign seafloor and one with a boulder strewn complex seafloor. Each area was surveyed before and after deployment of objects. The survey time intervals varied from two days to eight days. In this paper we present the trial and show examples of SAS images and change detection of the images.