Estimation of manganese crust coverage by seafloor image binarization

Abstract

Co-rich ferromanganese crust (Fe-Mn crust) distributed on seamounts in the western Pacific are potential economic resources for cobalt, nickel, platinum, and other rare metals. A prospective mining site can be evaluated based on the abundance and grade of Fe-Mn crusts in the site as well as requiring that topography be smooth enough for mining operations. We thereby characterized the spatial distribution of Cobalt-rich ferromanganese crust covering the summit and slopes of a seamount in the western Pacific, using acoustic backscatter from multi beam echo sounders (MBES) and seafloor video observation. Acquisition of seafloor photographs using a Deep-sea camera is an important process for checking the presence or absence of Fe-Mn crust deposits around the seamount. In order to perform a quantitative evaluation of seafloor images observed during the camera tow, we analyzed video data using image processing software which aids in distinguishing crust from sediments. The binarization of seafloor images was applied to seafloor images in order to exploit the methods of texture segmentation using texture filters and color-based segmentation using K-Means clustering. The images extracted via texture segmentation using texture filters reflect well the spatial variation of the central part of seafloor compared to original images, but the extraction of crustal shapes along each edge of seafloor photographs was not as defined. Whereas the images extracted by color-based segmentation using K-Means clustering reflect the spatial distribution of crust as a whole, the method is time consuming. Texture segmentation is faster, but failed to match the complicated boundary between the manganese crusts and sediments. We will provide enhanced estimates of the distribution of Fe-Mn crust on the seamount which were produced using these seafloor image analysis techniques.