Sea-floor classification using multibeam echo-sounding angular backscatter data: a real-time approach employing hybrid neural network architecture

Abstract

The presently studied numerical model, e.g., composite roughness, is successful for the purpose of seafloor classification employing processed multibeam angular backscatter data from manganese-nodule-bearing locations of the Central Indian Ocean Basin. Hybrid artificial neural network (ANN) architecture, comprised of the self-organizing feature map and learning vector quantization (LVQ), has been implemented as an alternative technique for sea-floor roughness classification, giving comparative results with the aforesaid numerical model for processed multibeam angular backscatter data. However, the composite-roughness model approach is protracted due to the inherent need for processed data including system-gain corrections. In order to establish that tedious processing of raw backscatter values is unessential for efficient classification, hybrid ANN architecture has been attempted here due to its nonparametric approach. In this technical communication, successful employment of LVQ algorithm for unprocessed (raw) multibeam backscatter data indicates true real-time classification application.