A Geometric and Radiometric-Invariant Matching Method for SSS and MBES Data

Matching multibeam echo sounder (MBES) data and side scan sonar (SSS) data has the potential to obtain high-accuracy seabed topography and geomorphological details simultaneously. However, the position uncertainty as well as radiometric and geometric distortion of SSS images bring serious challenges to the matching process. In this article, we proposed a novel matching method using sea bottom line matching and template matching to solve the above problems. First, we extracted the sea bottom points from the SSS image and calculated the depth series of the sea bottom line as the auxiliary information. Then, coarse matching is employed to this depth series and its corresponding terrain data of the MBES, to remove the coarse position uncertainty between the SSS and MBES images. After that, multiscale local self-similarity (MLSS) is proposed here, which is robust to geometric and radiometric distortion. Based on the coarse-matched SSS image, MLSS descriptor is applied for template matching between SSS and MBES images. Experiments verified the performance of the method. The high-resolution and high-accuracy seabed topography and surface details are eventually obtained.