The automatic fusion of classified sidescan sonar mosaics using CML-RTS and Markov random fields

This paper presents a framework for registering and fusing classified sidescan sonar data. It builds on recent advances in navigation and registration for improved mosaicing, applying novel fusion algorithms to integrate data from overlapping sidescan survey lines to produce large scale classified mosaics. While typical mine-counter-measures (MCM) and rapid environmental assessment (REA) missions provide various over-lapping views of the same region of seafloor, research on sidescan image analysis has traditionally concentrated on the analysis of individual images. The available information from the other images, relating to the same region of seafloor, is generally not considered. The image registration and mosaicing process allows this complementary data to be fused, producing an improved final classification result. The sidescan imagery is first pre-processed through the application of advanced radiosity correction algorithms. Following radiosity correction, texture segmentation for the data presented in this paper is achieved using features derived from the averaged normalised power spectral density. The individual classification maps are georeferenced and coregistered using a Concurrent Mapping and Localisation Rauch-Tung-Striebel (CML-RTS) procedure. This uses local landmarks within the individual images and the AUVs navigation data to generate a more accurate and smooth navigation trajectory. This trajectory is used to produce the registered classification mosaics. The coregistered classification results are then fused to produce an improved class mosaic for the entire survey region. The fusion model uses a voting scheme to initialize the seafloor map after which a Markov random field (MRF) model is used to produce the final fused classification mosaic. The entire process (classification, registration and fusion) is demonstrated on real sidescan data taken at the Saclant Centre, La Spezia, Italy.