Refinement of Seafloor Elevation Using Acoustic Backscatter

We propose an algorithm for the reconstruction of elevation and material property maps of the seafloor using a sidescan sonar backscatter image and sparse bathymetric points co-registered within the image. Given a path for the sensor; the reconstruction is corrected for the movement of the fish during the image generation process. To perform reconstruction, an arbitrary but computable scattering model is assumed for the seapoor backscatterer. The algorithm uses the sparse bathymetric data to generate an initial estimate for the elevation map which is then iteratively refined to fit the backscatter image by minimizing a global error functional. Concurrently, the parameters of the scattering model are determined on a coarse grid in the image by fitting the assumed scattering model to the backscaner data. The elevation surface and the scattering parameter maps converge to their best fit shape and values given the backscatter data. The reconstruction is corrected for the movement of the sensor by initially doing local reconstructions in sensor coordinates and then transforming the local reconstructions to a global coordinate system and performing the reconstruction again. The algorithm supports different scattering models, so it can be applied to different underwater environments and sonar sensors. In addition to the elevation map of the seafloor, the parameters of the scattering model at every point in the image are generated. Since these parameters describe material properties of the seafloor; the maps of the scattering model parameters can be used to segment the seafloor by material type.