Yasuhiro Tan ; Joo Kooi Tan ; Hyoungseop Kim ; Seiji Ishikawa
Abstract:Understanding the distribution of seafloor sediment using a side-scan sonar is very important to grasp the distribution of seabed resources. This task is traditionally carried out by a skilled human operator. However, with the appearance of Autonomous Underwater Vehicles, automated processing is now needed to tackle the large amount of data produced and to enable on the fly adaptation of the missions and near real time update of the operator. We propose in this paper a method that applies a subspace method and higher-order local auto-correlation feature to the acoustic image provided by the side-scan sonar to classify seabed sediment automatically. In texture classification, the proposed method outperformed other methods such as gray level co-occurrence matrix and Local Binary Pattern operator. Experimental results show that the proposed method produces a consistent map of a seafloor.